Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2401.10648

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Proceedings - 2024 IEEE 3rd Real-Time and Intelligent Edge Computing Workshop, RAGE 2024  

In response to the exponential growth of e-commerce, our paper addresses the transformation of warehouse operations towards mega warehouses, necessitating advanced digitalization. We focus on the integration and optimization of edge AI technologies to enhance operational efficiency, accuracy, and timeliness in logistic warehouses. We propose a novel edge AI architecture tailored to the processes of warehouse operations ensuring that digitalization aligns with operational workflows. Utilizing a real-world warehouse equipped with over 60 cameras as a testbed, we demonstrate the practical application and benefits of edge computing in logistics. Our experiments have shown significant potential improvements in energy efficiency and timeliness, crucial metrics for the successful integration of edge AI technologies in warehouses.

DOI： 10.1109/RAGE62451.2024.00010

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services  

Spatio-temporal data is utilized in various fields, but its scale is continuously growing, leading to significant labor and costs in storage and processing. Therefore, the value that can be derived from spatio-temporal data is diluted due to management costs. We propose a new data management flow using various metadata and common programs for spatio-temporal data utilization. Traditionally, various spatio-temporal data processing have been implemented and processed according to each spatio-temporal data. We defined spatio-temporal data structure metadata and performed data processing based on metadata using a common data processing program. Furthermore, we automated the generation of data structure metadata by combining our data skeleton recognition method and generative AI model. Using this flow, we expect to improve the sustainability of utilizing spatio-temporal data.

DOI： 10.1145/3643832.3661423

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services  

We present an assisting system for creating a ceiling plan. Conventional methods of creating a ceiling plan are time-consuming and high-cost. Our system requires only two inputs from a user and outputs the panoramic ceiling image that shows the whole ceiling surface. The system detects the ceiling fixtures and depicts them seamlessly for a reliable resulting image. We confirmed the possibility of assisting in creating a ceiling plan with our system through the experiment.

DOI： 10.1145/3643832.3661847

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

In the context of Kenyan Safari Game Drives, which traditionally rely on visual scouting and intermittent communication, challenges arise regarding the effectiveness of animal detection and environmental conservation. Consequently, animals experience stress, and tourists encounter difficulties in observation due to obstruction by other visitors. Addressing these challenges involves maintaining animal welfare while enhancing tourist satisfaction. To revitalize post-pandemic tourism and enrich safari experiences, this study proposes a wildlife spotting and sharing system. The proposed method integrates wildlife detection and sharing technologies guided by three core principles: preserving encounter serendipity, regulating information access, and promoting environmental conservation. The system architecture incorporates sensors, GPS, and tablet terminals to facilitate real-time data collection, animal identification, and information dissemination. This approach aims to foster an engaging, efficient, and environmentally conscious wildlife spotting and sharing experience, potentially establishing a model for sustainable wildlife tourism in comparable wildlife-rich regions.

DOI： 10.1007/978-3-031-59988-0_19

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

The manual creation of a ceiling plan consumes many human resources to confirm the current state of existing buildings for the renovation. Especially the positions and the types of existing fixtures are important. In this paper, we propose a synthesis method for a panoramic ceiling image using a video shot by an omnidirectional camera. We utilize a Visual SLAM method to map the positions of omnidirectional video frames including the ceiling. We prioritize the clear depiction of fixture outlines for easier identification of the types of ceiling fixtures. To draw clear fixture outlines in the resulting image, each fixture is referred from the single video frame that captures it most clearly. In the experiment, we collected an omnidirectional video in the indoor area of approximately 273 square meters with multiple rooms. We evaluated the synthesized image for positional accuracy of the ceiling fixtures, and our proposed method demonstrated higher accuracy compared to the baseline.

DOI： 10.23919/ICMU58504.2023.10412256

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

In distribution warehouses, various forms of data transformation are underway, such as the conversion of data related to receiving and inventory management. Distribution warehouses have designated areas known as truck berths, where trucks are parked for loading/unloading. While data collection is expected to enhance work efficiency at these truck berths, there is no data regarding the status of work in progress in the unloading space, and no system is in place to analyze the work. Consequently, it is unclear which tasks are problematic and how to enhance efficiency. To address this issue, this paper proposes a framework for analyzing the loading/unloading situation at truck berths with the objective of enhancing berth utilization efficiency and work efficiency. Specifically, we employed a fixed camera for object detection through instance segmentation. This approach enables us to understand the situation of trucks, unloading spaces, and workers. The training data were annotated using a large amount of real-world data, allowing for the measurement and analysis of the efficiency of loading/unloading operations. By calculating the amount of cargo based on the floor space, we assessed the efficiency of unloading operations per truck. Through the analysis of both efficient and inefficient operations, insights for enhancing work efficiency were provided.

DOI： 10.23919/ICMU58504.2023.10412228

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：ACM International Conference Proceeding Series  

In this demonstration paper, we present the RHS360, a 360-degree robotic hand system designed to facilitate communication across spatial domains. The RHS360 is capable of providing continuous alignment between the user's position in the cyber space and the robotic hand's position. Furthermore, it allows users in the cyber space to reposition the robotic hand to accommodate their movements within the space. Our system not only enables communication, including gestures, through the use of the robotic hand but also provides essential social cues that are crucial in effective communication.

DOI： 10.1145/3627050.3630730

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ACM International Conference Proceeding Series  

Traditional wildlife spotting on Kenyan Safari Game Drives, rooted in visual scouting and sporadic communication, encounters challenges in animal detection efficiency and environmental preservation. In light of rejuvenating post-pandemic tourism and enhancing safari experiences, this study proposes a wildlife spotting and sharing system. The methodology melds wildlife detection and sharing technologies, anchored by three core principles: preserving chance of encounters, regulating information accessibility, and endorsing environmental conservation. The system architecture employs sensors, GPS, and tablet terminals for real-Time data acquisition, animal identification, and information propagation. This approach aims to nurture an engaging, efficient, and eco-conscious wildlife spotting and sharing experience, potentially setting a benchmark for sustainable wildlife tourism in similar wildlife-rich regions.

DOI： 10.1145/3627050.3630727

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ACM International Conference Proceeding Series  

The rise of diverse digital environments like digital twins and the metaverse emphasizes the importance of telepresence methods for linking users across different spaces. These methods aim to boost communication and services between these spaces. This paper introduces RHS360, a 360-degree robot hand system designed for inter-spatial communication. RHS360 ensures the robot hand aligns precisely with the user in cyberspace, adjusting based on the user's movements in the virtual realm. This enables the robot hand not only to aid communication and gestures but also to convey crucial social cues, enhancing the overall communication experience. Additionally, a user survey was conducted, revealing that observing the robot hand's movements significantly improved understanding their partner's thoughts and fostered a greater sense of intimacy. These findings suggest that the proposed method enhances communication between different spaces.

DOI： 10.1145/3627050.3631573

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Communications in Computer and Information Science  

This research proposes a communication system called Mobility Link XR that connects physical space and cyberspace with mobility. Mobility Link XR is a system that enables remote users to view panoramic video from a 360-degree camera attached to a mobility vehicle in different space by wearing a VR device, and mobility users to view the remote user as an avatar by wearing an MR device. In this way, sharing space in three dimensions using XR enables a higher level of human communication. In this paper, we apply Mobility Link XR to an electric wheelchair and design two types of scenarios: an assistance mode that reproduces the positional relationship of communication in a conventional wheelchair, and a passenger mode that reproduces the positional relationship inside a vehicle, which is said to be a suitable distance for conversation. We also evaluated the reproducibility of communication in the wheelchair and the effectiveness of communication using the avatar. The results showed that the reproducibility of voice and emotion was highly evaluated and that the side-by-side positional relationship enabled higher quality communication as the avatar was more easily seen and felt present.

DOI： 10.1007/978-3-031-35992-7_43

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-031-34668-2_26

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/978-3-031-35602-5_25

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：International Conference on Intelligent User Interfaces, Proceedings IUI  

AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence. Therefore, we propose IRIS to perform Interpretable Rubric-Informed Segmentation on action sequences for AQA. We investigated IRIS for scoring videos of figure skating performance. IRIS predicts (1) action segments, (2) technical element score differences of each segment relative to base scores, (3) multiple program component scores, and (4) the summed final score. In a modeling study, we found that IRIS performs better than non-interpretable, state-of-the-art models. In a formative user study, practicing figure skaters agreed with the rubric-informed explanations, found them useful, and trusted AI judgments more. This work highlights the importance of using judgment rubrics to account for AI decisions.

DOI： 10.1145/3581641.3584048

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023  

Crowd flow forecasting is expected to have a wide range of applications such as human resource allocation, guidance design, marketing, disaster mitigation and congestion prediction for avoiding epidemic such as COVID-19. Crowd flow forecasting is challenging because it requires considering both the task of capturing the temporal dependency of data and capturing the spatial dependence. To address these challenges, in this paper, we propose a mechanism for referencing time-series features that are important for forecasting and incorporating graph convolution into Transformer, and we introduce CrowdFlowTransformer(CF-Transformer), a deep learning model based on Google's Transformer framework captures the Spatio-temporal dependency of time series. CF-Transformer captures the time series dependency by extracting important local time series from the past time series, inputting them to the decoder of Transformer, and encoding critical features into the model's input. We adapted CF-Transformer to a real-world crowd flow dataset. We evaluated it by comparing its forecasting accuracy with conventional models, and the results demonstrate that our model outperforms the conventional models.

DOI： 10.1109/PerComWorkshops56833.2023.10150301

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICAIIC57133.2023.10067079

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

<p>Various types of indoor positioning methods have been proposed. Accordingly, various ground-truth measurement methods have also been used. The selection of the ground-truth measurement method is crucial when appropriately evaluating the indoor positioning methods. In recent years, data-driven methods have been actively proposed, making the ground-truth measurement even more critical. Most existing survey papers only focus on classifying indoor positioning methods and datasets for evaluation. Our survey paper investigates the methods to measure the ground truth. First, we list the primary examples of ground truth measurement systems and summarize their characteristics, such as the scale, accuracy, and frequency when collecting ground truth. Then, we classify them into the following five categories: OMC, ToF/MFPS, SLAM, Manual, and Others. Their advantages and disadvantages are also discussed based on four indices: accuracy, coverage, device cost, and labor cost. Finally, we discuss the appropriate way to select the ground-truth measurement system for quantitative evaluation. Our survey will be the guideline for selecting an appropriate ground-truth measurement system for indoor positioning and indoor navigation.</p>

DOI： 10.2197/ipsjjip.31.15

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics  

<p>Dialogue systems that handle emotions have been shown to improve user satisfaction and increase positive interactions, and are expected to play a role as a digital partner that accompanies humans. In order for dialogue systems to recognize user emotions and express their own emotions, emotion regulation methods that select appropriate emotions from the context of the dialogue are indispensable. In this paper, we propose a text-based dialogue framework that performs emotion regulation, which estimates appropriate emotions during response. Our proposed method estimates appropriate emotions by considering the semantic and emotional context of the user’s text, and generates dialogue responses with emotional expressions using a neural network. The effectiveness of the proposed method was demonstrated through automatic evaluation using an evaluation metrics for the quality of dialogue generation and human evaluation by 100 subjects collected through a crowdsourcing.</p>

DOI： 10.3156/jsoft.34.3_568

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

DOI： 10.11517/jsaisigtwo.2022.swo-057_04

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Proceedings - SIGGRAPH 2022 Posters  

We introduce MetaPo, a mobile robot with spheric display, 360° media I/O and robotic hands for creating a unified model of interspace communication. MetaPo works as a portal between pairs of physical-physical, cyber-cyber and cyber-physical spaces to provide 1) panoramic communication for multiple remote users, and 2) immersive interspace migration with mobility functionality. The paper overviews our concept and first prototype of MetaPo with its hardware and software implementation.

DOI： 10.1145/3532719.3543255

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

Workloads in logistics warehouses have been increasing to meet growing demand, and a labor shortage has become a problem. Utilizing information of laborer locations leads to an increase in productivity. We propose an integrated positioning method using solar-powered Bluetooth Low Energy (BLE) beacons. They are easy to install and maintenance-free since they can work without power sources. However, their advertisement interval depends on illuminance and is unstable. Moreover, there are many obstructions in warehouses, such as shelves and products, which cause signal attenuation, interference, and packet losses. We apply particle filters, map matching, and speed prediction with a neural network model to improve robustness and accuracy. We installed 94 beacons in a logistics warehouse. We evaluated the accuracy and found that our method is more accurate than a baseline method.

DOI： 10.1007/978-3-031-05463-1_21

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Language：English  

DOI： https://doi.org/10.1109/IOTM.001.2100143

Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE Sensors Letters  

Autonomous mobile robots (AMRs), including delivery robots, security robots, and automated wheelchairs, are very promising for improving the quality of life, safety, mobility, and productivity. To deploy such sensor-rich AMRs in real-world society, we have to resolve not only technical challenges but also ethical and societal issues. In addition, since multiple companies are developing different types of AMRs individually, we have to design and develop practical platform to enable safe cooperation and collaboration before using them in the society. Hence, in this letter, we present a viable Nagoya Experimental Site and Testbed named <italic>RobotNEST</italic> developed at Nagoya City, Japan, where we leverage vehicular communications and edge/cloud computing technologies to develop cooperative mobile robots. RobotNEST provides real-world AMRs, experimental fields, learning kits, and infrastructural sensors and communications, including 5G private networks. We also introduce multiple developed applications at RobotNEST that enable safe cooperation for autonomous robots. Finally, we discuss the scalability and limitations of the testbed and conclude with future research directions.

DOI： 10.1109/LSENS.2021.3139624

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST  

Recently, there has been an increasing demand for traffic simulation and congestion prediction for urban planning, especially for infection simulation due to the Covid-19 epidemic. On the other hand, the widespread use of wearable devices has made it possible to collect a large amount of user location history with high accuracy, and it is expected that this data will be used for simulation. However, it is difficult to collect location histories for the entire population of a city, and detailed data that can reproduce trajectories is expensive. In addition, such personal location histories contain private information such as addresses and workplaces, which restricts the use of raw data. This paper proposes Agent2Vec, a mobility modeling model based on unsupervised learning. Using this method, we generate synthetic human flow data without personal information.

DOI： 10.1007/978-3-030-94822-1_36

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Smart Innovation, Systems and Technologies  

The implementation of wearable airbags to prevent fall injuries depends on accurate pre-impact fall detection and a clear distinction between activities of daily living (ADL) and them. We propose a novel pre-impact fall detection algorithm that is robust against ambiguous falling activities. We present a data-driven approach to estimate the fall risk from acceleration and angular velocity features and use thresholding techniques to robustly detect a fall before impact. In the experiment, we collect simulated fall data from subjects wearing an inertial sensor on their waist. As a result, we succeeded in significantly improving the accuracy of fall detection from 50.00 to 96.88%, the recall from 18.75 to 93.75%, and the specificity 81.25 to 100.00% over the baseline method.

DOI： 10.1007/978-981-19-0361-8_8

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IEEE Intelligent Vehicles Symposium, Proceedings  

In this research, we proposed an active management method to tele-monitor and tele-operate more autonomous vehicles (AVs) with few observers by adjusting the movement of the AVs actively. A management system is created to get the status from the AVs and separate the monitoring requirement to the observers optimally. When the requirements might be intensive, the management system can also adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles. We implement and verify the management system in an autonomous driving simulator - CARLA for the limited number of AVs and observers. Based on the data acquired from the driving simulator, we also create a numerical simulator and tested our method with the pairs of a large number of AVs and observers. The result of both simulators shows that our method can reduce the utilization degree of observers and make them monitor more AVs.

DOI： 10.1109/IV48863.2021.9575997

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

<p>This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.</p>

DOI： 10.2197/ipsjjip.29.58

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：13th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2021  

With the rapid development of Micro Electro-Mechanical Systems (MEMS) technologies, indoor navigation and localization with Inertial Measurement Unit (IMU) has been increasingly feasible. IMU-based indoor localization is a low-cost, energy-efficient, and infrastructure-free approach. There are various methods for it, and most of them require gravity estimation (e.g. the projection of angular velocity, the extraction of horizontal acceleration). In particular, when you use a smartphone, it changes the orientation of its IMU frequently, therefore the gravity estimation needs to be more robust to sensor orientation and its noise. In this paper, we propose a gravity estimation method based on deep learning called RGNet (Robust Gravity Estimation Neural Network) that is robust to sensor orientation and noise. We train an LSTM (Long short-term memory)-based neural network that estimates gravity from acceleration and angular velocity. Furthermore, for the problem that it is difficult to prepare the ground truth of gravity directly, we propose a method to train the gravity estimation neural network indirectly using the heading, taking advantage of the fact that the heading can be estimated from the gravity and angular velocity. The evaluation results show that the accuracy of the proposed method outperformed the baseline gravity estimation method (Android API, Low-pass filter, and Extended Kalman filter). We also confirmed that the accuracy of IMU-based localization is affected by the difference in gravity estimation methods.

DOI： 10.23919/ICMU50196.2021.9638853

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/978-3-030-76063-2_41

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DOI： 10.23919/ICMU50196.2021.9638789

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IEEE  

DOI： 10.1109/IV48863.2021.9575499

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer  

DOI： 10.1007/978-3-030-77015-0_22

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Smart Innovation, Systems and Technologies  

The paper presents a ballroom dance figure classification method with LSTM using video and wearable sensors. Ballroom dance is a popular sport among people regardless of age or sex. However, learning ballroom dance is very difficult for less experienced dancers as it has many complex types of “dance figures”, which is a completed set of footsteps. Therefore, we aim to develop a system to assist dance exercise which gives advice proper to each dance figure characteristic by recognizing dance figures correctly. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the images of dancers overlap each other. To solve the problem, we propose a hybrid figure recognition method combining video and wearable sensors to enhance its accuracy and robustness. We collect video and wearable sensor data of seven dancers including acceleration, angular velocity, and body parts location change by pose estimation. After that, we preprocess them and put them into an LSTM-based deep learning network. As a result, we confirmed that our approach achieved an F1-score of 0.86 for 13 figure types recognition using the multi-modal sensors with trial-based fivefold cross-validation. We also performed user-based cross-validation, and sliding window algorithms. In addition, we compared the results with our previous method using Random Forest and also evaluated the robustness with occlusions. We found the LSTM-based method worked better than Random Forest with keypoint data. On the other hand, LSTM could not perform well with a sliding window algorithm. We consider the LSTM-based method would work better with a larger dance figure data, which is our next work. In addition, we will investigate how to solve occlusion problems with pose estimation.

DOI： 10.1007/978-981-15-8944-7_13

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japan Society of Mechanical Engineers  

<p>In this research, we proposed an active management method to monitor more autonomous vehicles (AVs) remotely with few observers. A management system is created to get the status from the AVs and separate the monitoring requirement to the observers optimally. When the requirements might be intensive, the management system can adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles. Based on the data acquired from the driving simulator, we create a numerical simulator and test our method with different pairs of a large number of AVs and observers. The result showed that our method can reduce the utilization degree of observers and make them monitor more AVs.</p>

DOI： 10.1299/jsmermd.2021.2p1-b10

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Lead author   Language：English   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

DOI： 10.1007/978-3-030-50344-4_33

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Other Link： https://dblp.uni-trier.de/db/conf/hci/hci2020-23.html#YonezawaSKN20

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electrical Engineers of Japan  

<p>Bus transportation service is more influenced than other public transport by various factors such as traffic congestion, weather condition, number of passengers, traffic signals. These factors often cause delay and the users may feel inconvenience while waiting at the bus stop. In the case of snowfall event, a large delay occurs, which greatly reduces the convenience of the bus. This paper aims at highly accurate arrival time prediction for each bus stop section in snow event in urban area. We investigate vulnerability of bus operations to snowfall and incorporate into predictions using geographical characteristics. In each bus stop section, we estimate geographical characteristics (gradient angle and gradient direction) and snow accumulation amount with detailed spatial resolution as factors affecting bus delay. Then, we evaluate a prediction accuracy using the arrival time prediction model with multiple regression analysis and the Kalman filter. As a result of the multiple regression analysis, it was found that the geographical characteristics of each bus stop section were the explanatory variables that greatly affect the bus delay at snowfall event. Furthermore, we predicted the bus arrival time using actual bus operation data. Of the 29 routes, 18 routes showed improvement in the predicted arrival time.</p>

DOI： 10.1541/ieejeiss.140.257

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1145/3410530.3414333

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019  

Indoor location estimation has long been researched to realize location-based services. In this paper, we propose an indoor location estimation method for Bluetooth Low Energy (BLE) devices using end-to-end LSTM neural network. We focus on large-scale exhibition where is a tough environment for wireless indoor location estimation due to signal strength instability. To achieve higher accuracy, deep learning based methods are proposed rather than trilateration or fingerprint. Existing deep learning based methods estimate the location from the probabilities using the difference of query signal strength and autoencoder-reconstruction of it. Proposed method adopts end-to-end location estimation, which means the neural network takes a time-series of signal strength and outputs the estimated location at the latest time in the input time-series. We also build a loss function which takes how a person walks into account. Considering the difficulty of data collection within a short preparation term of an exhibition, the data generated by a simple simulation is used in the training phase before training with a small amount of real data. As a result, the estimation accuracy is average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. Proposed method outperforms our previous trilateration based method's 4.51m average.

DOI： 10.23919/ICMU48249.2019.9006638

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Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

Authorship：Last author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：2019 Joint 8th International Conference on Informatics, Electronics and Vision, ICIEV 2019 and 3rd International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2019 with International Conference on Activity and Behavior Computing, ABC 2019  

In this paper, we propose a hybrid activity recognition method for ballroom dance exercise using video and wearable sensor. The purpose of our research is to design a mechanism to support ballroom dance exercise, and this paper reports the first part to design a mechanism is to support ballroom dance exercise, and this paper reports the first outcome to achieve the purpose - recognizing ballroom dance exercise. There are two conceivable ways to recognize dance exercise: videos and wearable sensor. However, each of them has its disadvantages. Using video is a good way to recognize the movement of the body. However, it cannot provide us accurate timing or strength of foot actions because the number of their flames per seconds is too small to recognize the fast movements of dancers. On the other hand, while a wearable sensor is good at recognizing foot timing and strength, it is not good at recognizing the movement of the whole body. Therefore we propose a hybrid recognition method utilizing the merits of both video and wearable sensor. This paper focuses to recognize four different types of steps in Latin American, a kind of ballroom dance. For each step, we record wearable sensing data and videos. As a result, it is found that the accuracy of step recognition is improved by adding wearable sensing data to video data shot from two different angles.

DOI： 10.1109/ICIEV.2019.8858524

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：CEUR Workshop Proceedings  

Most of the current inertial positioning systems can be categorized as the strapdown algorithm and the step-and-heading approach. However, for the strapdown algorithm using smartphone as a sensor device, the accuracy of the current MEMS based accelerometer are not enough for estimating relative movement. Also, for the step-and-heading approach, robust estimation of step length is always difficult. In this paper, we propose an end-to-end walking speed estimation method using Deep Learning to overcome these problems. By using our method, we can achieve a smartphone PDR with higher accuracy and better robustness to gait type.

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DOI： 10.1145/3341162.3343765

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

<p>In recent years, the importance of location information has increased due to the popularization of terminals such as smartphones. Our purpose is to estimate the 3D position of smartphones within several centimeters. This location information can reveal a person's behavior patterns and subject of interest. A method based on dynamic magnetism can estimate the 3D position of a terminal within several centimeters, and it is robust against the environment. However, since the performance of the smartphone's magnetic sensor is limited, this method cannot be used in smartphones. In this research, we propose a smartphone 3D positioning method using a Spinning Magnet Marker (SMM) that spins a powerful neodymium magnet with a motor. Magnetoquasistatic field generated by an SMM is detected by a magnetic sensor mounted on a smartphone, and the 3D position of a smartphone based on the SMM is estimated from the magnetism. We developed an SMM with a stepper motor to improve estimation accuracy. In addition, we studied the magnetism generated by an SMM and derive equations to estimate the 3D position of a smartphone. Furthermore, we improved the estimation accuracy and expanded coverage by introducing a noise reduction method. We evaluate the estimation accuracy of the proposed method. When the azimuth angle is 0^°, the elevation angle is 0^°, and the distance is 3m, the azimuth angle is estimated with a mean error of 1^°, the elevation angle is estimated with a mean error of 4^°, and the distance is estimated with a mean error of 9cm.</p>

DOI： 10.2197/ipsjjip.27.10

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DOI： 10.1145/3341162.3344852

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

DOI： 10.2197/ipsjjip.27.1

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/PERCOMW.2019.8730820

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IEEE  

DOI： 10.1109/PERCOMW.2019.8730728

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：ACM  

DOI： 10.1145/3307334.3328624

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：ACM  

DOI： 10.1145/3307334.3328719

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Springer  

DOI： 10.1007/978-3-030-13001-5_3

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DOI： 10.1145/3307334.3328667

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：CEUR Workshop Proceedings  

Most of the current inertial positioning systems can be categorized as the strapdown algorithm and the step-and-heading approach. However, for the strapdown algorithm using smartphone as a sensor device, the accuracy of the current MEMS based accelerometer are not enough for estimating relative movement. Also, for the step-and-heading approach, robust estimation of step length is always difficult. In this paper, we propose an end-to-end walking speed estimation method using Deep Learning to overcome these problems. By using our method, we can achieve a smartphone PDR with higher accuracy and better robustness to gait type.

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DOI： 10.1145/3267305.3267533

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DOI： 10.1109/ACCESS.2018.2865490

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DOI： 10.1145/3267305.3267532

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IEEE  

DOI： 10.1109/IGARSS.2018.8518719

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DOI： 10.3390/rs9080851

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Proceedings of IEEE Sensors  

This study presents a water-level sensor system that has multiple functions: real-time river-level monitoring and high accurate flood prediction for urban complex water flow (CWF) flooding caused by Localized Heavy Rain. Traditional water-level sensors for large-scale river can detect water-level rising, though they have limitations in detecting urban river dyke height to alert about flooding. This is a result of various riverside environmental limitations. Additionally, previous prediction methods are difficult to detect CWF flooding with high accuracy because water rising differ depends on a location's environment. Therefore, we propose a detection scheme for CWF by developing a water-level sensor system that works in various installation environments using infrared image processing with both low installation and operation cost. We implemented a CWF prediction system that produces accurate and early predictions using a Linear Regression (LR) of deep learning approach with data assimilation. Evaluating based on actual installations (over 1,000 days, 13 locations), our system proved to accurately monitor and predict water-level rising after 5 minutes.

DOI： 10.1109/ICSENS.2016.7808626

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DOI： 10.1109/CVPRW.2017.197

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DOI： 10.1145/3123024.3124410

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DOI： 10.1145/3123024.3124405

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DOI： 10.1145/3123024.3124406

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

Nearby event data, such as those for exhibitions and sales promotions, may help users spend their free time more efficiently. However, most event data are hidden in millions of webpages, which is very time-consuming for a user to find such data. To address this issue, we use web mining that extracts event data from webpages. In this paper, we propose and discuss the implementation of Event.Locky – a system for extracting event data from webpages in a user-defined area and displaying them to a user in a spatial-temporal structure. Furthermore, we design two core algorithms for event data extraction in Event.Locky: webpage-data-record extraction and event-record classification. The former is used to convert a semi-structural HTML document into processable structured data. The latter filters out non-event data from extracted data records using machine learning. We trained and evaluated Event.Locky with an actual dataset composed by 96 restaurants and shops at Nagoya train station. As a result, our event-classification algorithm achieved an F1 score of 91.61%, an increase of 3.07% from current event-classification algorithms. The combination of our event-classification algorithm and our data-record-extraction algorithm achieved F1 score 83.96% to extract event records from webpages. That increased 1.6% from current algorithm. Finally, we discuss the feasibility of Event.Locky in an actual online environment through the implementation of a demonstration application.

DOI： 10.2197/ipsjjip.25.321

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.23919/ICMU.2017.8330076

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本知能情報ファジィ学会  

DOI： 10.3156/jsoft.28.6_169

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery  

Techniques for obtaining customers' behavior (dwell time, count, and flow) in a shopping mall or large exhibit are highly sought-after by organizers or shop owners. Additionally, ways of effectively directing customers from cyber space such as the Web or smartphone apps to physical retail stores are also in high demand. "Online to Offline (O2O) Marketing" has recently been attracting attention to make these a reality. However, the know-how accumulated through "O2O Marketing" is not shared widely as it can be sensitive for business and customer's privacy. In this paper, we provide the knowledge obtained through the demonstration experiments held by the "O2O Digital Marketing Study Group" organized by NPO Lisra at a large underground shopping mall in Nagoya. 250 BLE tags and 12 Wi-Fi scanners were installed in this mall. We also organized a "coupon campaign" to increase the number of participants. We were able to effectively collect visiting customer count measurements via Wi-Fi scanners irrespective of the anonymization of BSSID, as our results showed similar trends collected by optical human detectors inherently installed at the venue. This paper provides preliminary insight into understanding the behaviors of retail shoppers and we believe this is a firm starting point for this area.

DOI： 10.1145/2994374.2994403

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

There are many methods for indoor positioning. These methods are divided into the relative localization and absolute localization. In the relative localization, one widely used method is Pedestrian Dead Reckoning (PDR). Relative localization estimates the moving distance, orientation, and height of the pedestrian. However, relative localization has a problem caused by an accumulated error: the longer the path, the worse the accuracy of relative localization. There is another problem in the existing evaluating metrics: they compare only the actual location and the estimated location of the destination. Relative localization also has this evaluation problem. We propose PIEM: Path Independent Evaluation Metric for Relative Localization. PIEM is a path independent evaluation metric, considering the complexity of the path
distance, orientation, and height. Then we evaluate these three factors of relative localization in addition to the position. Our proposed method showed more consistent results for the complexity of the path than the existing methods of relative localization evaluation.

DOI： 10.1109/IPIN.2016.7743587

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DOI： 10.3390/rs8110967

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DOI： 10.1007/978-981-10-2278-4_18

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

PDR (Pedestrian Dead Reckoning) is a very promising technology for indoor positioning. We held a technical challenge, entitled the UbiComp/ISWC 2015 PDR Challenge, consisting of the following three categories: Algorithm, Evaluation, and Exhibition. In this paper, we specially focus on collected data for PDR algorithm category. UbiComp/ISWC participants equip smartphone by hand and walk around the particular floors. The pedestrian sensing data is collected. The number of our prepared route pattern is 5. Over two days, we collected the sensing data of 105 subjects. The total number of collected pedestrian sensing data was 343. After removing faulty data, this number was 241 and the number of unique subjects was 103. Sensor types are accelerometer, gyro, magnetic, and air pressure. We, PDR Challenge organizers, provide the corpus as UbiComp/ISWC 2015 PDR Challenge Corpus. The corpus contains ground truth of 3-D coordinates of the pedestrian sensing data for each second.

DOI： 10.1145/2968219.2968276

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

Current motion sensors in wearable devices are primarily used for simple orientation and motion sensing. They provide however signals related to more complex and subtle human behaviours which will enable next-generation human-oriented computing in scenarios of high societal value. This requires large scale human activity corpuses and improved methods to recognise activities and their context. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing robust activity and context recognition methods and evaluating systems in the real world. As a special topic, we wish to reflect on the challenges and approaches to recognise activities outside of a pre-defined set to achieve an open-ended activity and context awareness. Following the success of previous years, this workshop is the place to share experiences on human activity corpus and their applications and to discuss the future of activity sensing, in particular towards open-ended contextual intelligence.

DOI： 10.1145/2968219.2968294

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

We present a scheme that improves accuracy of 2.4GHz RF tag based indoor positioning. The accuracy of indoor positioning using 2.4GHz RF tags is affected by propagation loss caused by human body shielding, especially in crowded situations. This paper proposes a RSSI compensation scheme that estimate a crowd density level based on detected 2.4 GHz RSSI. We developed a scanner system to sense BLE (Bluetooth Low Energy) tag for 2.4 GHz RSSI compensation. We deployed 40 BLE scanners and over 100 BLE tags, and collected data for 100 participants at an actual event over a period of four days. Using the collected data, we estimated crowd density level that can be applied to the proposed compensation scheme based on theoretical methods on propagation loss. Our scheme resulted in 59.8% higher accuracy than simple positioning method without the compensation.

DOI： 10.1145/2968219.2968293

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：ASSOC COMPUTING MACHINERY  

Indoor location estimation is essential technology when we analyse the participants' activities in large-scale exhibition. There are some problems with existing methods such as PDR, ultrasound and laser range finder: installation location of measurement equipment at large site, cost for measurement equipment, and necessity of smartphone application. We focus on Bluetooth Low Energy(BLE). Currently, BLE technology is used as proximity notification for smart phones and cannot detect the exact distance between two BLE devices. This is because BLE radiowave is unstable and signal strength changes every time at the same distance. In this paper, we propose a location estimation method which utilizes BLE beacon tags and single board computers. In contrast to conventional ways using location-fixed BLE beacons, BLE beacon tags are distributed to participants at the event. Signal strengths of BLE advertising packets captured by multiple location-fixed scanners are used for location estimation. The method requires less cost for equipment and labor of installing an application. It can be used without complex initial setting. We had a data collection experiment at real large-scale exhibition. We estimate locations of participants by applying the proposed method to the collected data, then evaluate accuracy of estimation and analyse the activities such as the time of longer stay booths based on participants' occupation. As a result, we could track the movement of the participants to some meters error and find the characteristic booths based on longer-stay booths per occupations.

DOI： 10.1145/3004010.3004051

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

Human activity recognition by wearable sensors will enable a next-generation human-oriented ubiquitous computing. However, most of the existing research on human activity recognition is based on a small number of subjects, and lab-created-data. To overcome this problem, we hold HASC Challenge as a technical challenge to collect the data for activity recognition. In addition to HASC Challenge, we collected indoor pedestrian sensing data of 107 people with a balance of gender and age (HASC-IPSC). Through these data collection, we gathered 111,968 sensor files of 510 subjects. For the convenience of the future researchers in this field, we combined them as a single corpus named HASC-PAC2016 and make it public. Baseline recognition result of HASC-PAC2016 segmented data is 73.4% accuracy for overall, 81.4% for limited by terminal position, and 85.1% with file-based recognition. For sequence data, we only get 73.4% even for limited subjects. This shows we need further research of activity recognition using HASC-PAC2016.

DOI： 10.1145/2968219.2968277

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This study presents a water-level sensor system that has multiple functions: real-time river-level monitoring and high accurate flood prediction for urban complex water flow (CWF) flooding caused by Localized Heavy Rain. Traditional water-level sensors for large-scale river can detect water-level rising, though they have limitations in detecting urban river dyke height to alert about flooding. This is a result of various riverside environmental limitations. Additionally, previous prediction methods are difficult to detect CWF flooding with high accuracy because water rising differ depends on a location's environment. Therefore, we propose a detection scheme for CWF by developing a water-level sensor system that works in various installation environments using infrared image processing with both low installation and operation cost. We implemented a CWF prediction system that produces accurate and early predictions using a Linear Regression (LR) of deep learning approach with data assimilation. Evaluating based on actual installations (over 1,000 days, 13 locations), our system proved to accurately monitor and predict water-level rising after 5 minutes.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

PDR (Pedestrian Dead Reckoning) is a very promising technology for indoor positioning. We held a technical challenge, entitled the UbiComp/ISWC 2015 PDR Challenge, consisting of the following three categories: a PDR algorithm category; a PDR Evaluation method category; and an exhibition. In this paper, we especially focus on several systems for the PDR algorithm category. A PDR skeleton was prepared for the participants. Using an Android skeleton, participants focus on implementing the PDR algorithm because of the skeleton's various functions, such as sensor data acquisition, trajectory visualization, and sensor data upload. The evaluation server evaluates the accuracy of each PDR algorithm automatically as often as sensor data is uploaded to the server and provides a trajectory image file so that participants can compare their PDR algorithms in real time.

DOI： 10.1109/ICMU.2016.7742094

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

We propose Landmark-Conscious Voice Navigation as one type of a pedestrian navigation system, which navigate users by only voice guidance. It is necessary to standardize data model in order to use this system widely. In a previous paper[1], we constructed a basic voice navigation system, which uses Open Street Map based data model. In this paper, at first, we conduct an experiment of voice navigation at an underground shopping area of Nagoya Station with two types of landmark descriptions. After that, we discuss what data structure is necessary to describe landmark information for voice navigation. Therefore, we propose to extend IndoorGML1.0 by adding landmark space as a new defined data model for voice navigation. The main contribution of this paper is that we conduct an experiment of voice navigation and research how different landmark descriptions affect users
furthermore, we discuss a IndoorGML extended data model for voice navigation.

DOI： 10.1145/2834812.2834814

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

In this work, we implement a mobile system called Velobug to measures 3D data of the environment. Velobug could generate dense and colored point cloud to reconstruct the environment for 3D mapping with long effective range. Velobug is mainly consisted of a Velodyne HDL-32e LiDAR senor and a Point Grey Research Ladybug3 panoramic camera. The LiDAR sensor is utilized to acquire 3D data of the environment and generate point cloud. Pictures captured by the panoramic camera are used for complementing RGB information of the point cloud after the calibration between the panoramic camera and the LiDAR sensor. We could maker point cloud denser by registering multiple frames of point cloud with Generalized Iterative Closet Point(Generalized-ICP) method. We evaluate Velobug in an underground shopping street near Nagoya Station. The experiment result shows the mobility, practicability and efficiency in reconstruction for 3D indoor mapping.

DOI： 10.1145/2800835.2800878

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

Since the advent of smartphones equipped with sophisticated sensing hardware, human activity recognition research has moved from utilizing dedicated sensing devices to using commercial smartphones. This paper presents the design of an algorithm to recognize and estimate travel distance when riding an elevator and its corresponding implementation within an app for a smartphone running Apple's mobile operating system (iOS). The algorithm receives solely the signal of the smartphone's accelerometer, recognizes that it belongs to an elevator ride, and proceeds to calculate the distance traveled by the rider. The algorithm has been designed in a way that simplifies the necessary operations to calculate the travel distance with the objective of minimizing processing power, while keeping the corresponding estimations highly accurate. This is an initial attempt towards the building of a robust, but simple and fast, real-time human activity recognition service on a wearable platform.

DOI： 10.1145/2800835.2801620

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大規模駅周辺は複雑な立体構造を構成しており,店舗や構造の情報が集約されていないため,利用者のプロファイルや目的に応じた情報提供が適切になされていない.そこで,ICT を活用し大規模駅において利用者の多様な状況に対応し,適切な店舗や経路の推薦やナビゲーションを行う総合的な情報提供・屋内案内サービスを実現する.また,利用者の利便性を向上させると同時に,駅関連事業者との適切な情報流通を実現し,地域社会の活性化を促進する.特に,東京や大阪と並んで複雑な構造をしている名古屋駅周辺を対象とし,次世代位置情報サービスを提供するスマートステーション化することを目指す.本研究は,1.無線 LAN・磁気・PDR を統合した屋内位置推定,2.音声に基づいて経路案内を行うナビゲーションシステム,3.目的志向の店舗推薦システム,4.店舗情報の継続的更新を可能にするクラウドソーシングシステムといった 4 つのサブテーマから構成される.

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Because GPS has poor accuracy indoors, indoor positioning method is required. In this research, we propose pedestrian route estimation method using magnetic. The route is limited in indoor environment and every route has different magnetic. We walk the route and collect the magnetic, and utilize them for route estimation. As estimation method, we adopt particle filter. We estimate the route by comparing candidate route data and observed data by particle filter. We evaluate this method in real environment. The result shows that the route is estimated in high accuracy better than 97% as far as there are few candidates.

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Web of Science

Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes an event data extraction method that extracts business event data, such as coupons, tickets, sales campaigns, etc., from a homepage or blog of shops and pushes them to users. Users no longer need to browse their favorite shops' homepage one by one. The method supports comprehensiveness and effectiveness for event data obtainment. This proposition works into two tasks: web page block segmentation and event data identification. The first task segments the web page into blocks. Each of the blocks includes information, such as title, notification, date, etc. relating to event information. Many related works suppose web page block segmentation based on specific tags, vision, function, etc. In this research, we propose a web page block segmentation method based on HTML document structure analysis. The second task is used to identity event data from segmented blocks. We propose a method to implement event data identification based on machine learning. We show the results of a verification experiment. Experimental data are from 96 shops located in two underground shopping streets UNIMALL and ESCA, at a train station in the city of Nagoya (Japan). Because the event data identification depends on the Japanese language, this method is available for all the Japanese home page.

DOI： 10.1109/COMPSAC.2015.235

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/2800835.2800940

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/2800835.2800940

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都市域での豪雨が起こす市街地の浸水被害を軽減するためには,浸水の発生箇所やその浸水深のような,浸水状況の情報提供が有効である.浸水状況を知ることで,道路上の危険箇所の歩行やアンダーパスでの車両の水没からの回避が可能になると考えられる.しかし,既存で用いられる水位計は定点観測用途が主であり,観測地点数が限られるため,観測点以外の浸水状況が把握できない.また,豪雨の発生時には浸水の様子を撮影した画像がインターネット上で多数共有されるが,浸水深を検出するためには量水板のように水位と比較可能な目安となる対象物が必要となるため,このような画像から浸水深を知ることが難しい.そこで,市街地における観測点以外の地点での浸水深の把握を目的に,インターネット上から収集した市街地の浸水の様子を撮影した画像を活用して,浸水深を検出する手法を提案する.本手法は,画像に写る人物の大きさと水位を比較し,人への影響に応じて膝以下,股下以下,胸部以下,胸部以上という分類で浸水深を推定することで,目安となる対象物を必要とせず,浸水深の検出を可能にする.提案手法を用いて,過去に市街地で発生した実際の浸水被害における浸水深を検出し,その結果を述べる.

Language：English   Publishing type：Research paper (scientific journal)  

複数の歩行センシングデータを用いて建物構造を生成するためには,歩行センシングデータから歩行軌跡を正確に推定できなければならない.本稿では,右左折や蛇行を伴わない歩行が一定時間以上安定してセンシングできている区間を安定歩行区間と定義し,それに基づいて歩行軌跡を推定する手法を提案する.一般的に人は合理的に行動するため,安定歩行時には直線的な軌跡をたどると考えられる.また屋内の通路の多くは直線的である.よって,混雑した通路や複雑な形状の建物の歩行を除けば,移動の大半は直線的な歩行とみなせる.安定歩行区間の検出には角速度を用いる.検出された安定歩行区間を直線的な歩行とみなし,角速度に乗っているオフセット値を求め,ドリフトを除去する.また,多くの通路の曲がり角の角度が 90 度であるという建物知識を導入し,安定歩行区間同士が平行または垂直になるように歩行軌跡を修正する.さらに,安定歩行区間が一定以上の割合となる歩行センシングデータを,高精度の歩行軌跡推定が期待できる信頼性の高いデータみなす.屋内歩行センシングコーパス HASC-IPSC を用いて提案手法の評価実験を行い,歩行軌跡の推定手法と信頼性の高い歩行センシングデータの発見手法の双方で有効性を確認した.信頼性の高いデータに限定すれば,歩行軌跡末端における進行方向の誤差平均は約 7 度となり,90 度以上の大きな誤差になる割合は約 1 %となった.

Language：English   Publishing type：Research paper (scientific journal)  

都市域で発生する豪雨は,市街地の浸水を招き,避難者の歩行困難やアンダーパスでの車両の水没等,道路交通に大きな被害を与える.特に中小河川や水路から溢れる水は,一般市町村道のような生活道路の浸水を招く.浸水の発生箇所や浸水深の情報提供は,歩行者や車両の被害軽減に有効であるため,生活道路での浸水の情報提供を実現を目指し,安価で簡易に構築可能な浸水観測ネットワークFloodEyeの開発と実環境での運用を行っている.しかし,豪雨や浸水被害が拡大すると,市街地では停電が発生することが多く,浸水観測やデータ提供の継続が難しい.そこで本研究は,被害拡大後も安定的に運用でき,生活道路の浸水被害軽減に活用できる浸水観測システムの開発を行う.本システムは,豪雨発生前に太陽光から蓄電した電力を活用し,停電時に観測とFloodEyeへのデータ伝送をする自律型電源機能をもつ.本システムを実環境に設置して,観測とデータ伝送を行い,連続した降雨日での電力消費量を計測した.この計測結果をもとに,同程度の降雨発生時に安定的な運用を実現するための効率的な電力使用の分析を行う.近傍の降水量や警報の発表状況から推定する被害拡大の可能性,観測結果からの浸水の発生状況を用いた,観測とデータ伝送の制御開始タイミングの決定と時間間隔の最適化の考察結果を述べる.

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人情報処理学会  

都市域で発生する豪雨は,市街地の浸水を招き,避難者の歩行困難やアンダーパスでの車両の水没等,道路交通に大きな被害を与える.特に中小河川や水路から溢れる水は,一般市町村道のような生活道路の浸水を招く.浸水の発生箇所や浸水深の情報提供は,歩行者や車両の被害軽減に有効であるため,生活道路での浸水の情報提供を実現を目指し,安価で簡易に構築可能な浸水観測ネットワーク FloodEye の開発と実環境での運用を行っている.しかし,豪雨や浸水被害が拡大すると,市街地では停電が発生することが多く,浸水観測やデータ提供の継続が難しい.そこで本研究は,被害拡大後も安定的に運用でき,生活道路の浸水被害軽減に活用できる浸水観測システムの開発を行う.本システムは,豪雨発生前に太陽光から蓄電した電力を活用し,停電時に観測と FloodEye へのデータ伝送をする自律型電源機能をもつ.本システムを実環境に設置して,観測とデータ伝送を行い,連続した降雨日での電力消費量を計測した.この計測結果をもとに,同程度の降雨発生時に安定的な運用を実現するための効率的な電力使用の分析を行う.近傍の降水量や警報の発表状況から推定する被害拡大の可能性,観測結果からの浸水の発生状況を用いた,観測とデータ伝送の制御開始タイミングの決定と時間間隔の最適化の考察結果を述べる.

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人情報処理学会  

近年のモバイルコンピューティング分野で盛んな研究テーマとして,屋内位置推定や建物構造の自動推定が挙げられる.しかし,現在までにこれらの研究で共通して利用可能なデータは存在せず,新たな手法を提案しても,既存研究との精度の比較検討が困難であった.そこで我々は,屋内位置推定研究と建物構造推定研究を主なターゲットとした,屋内歩行センシングコーパス (HASC-IPSC) を構築した.このデータ収集対象環境として選定した建物は,名古屋大学 IB 電子情報館の北棟 (B1F-5F),中棟 (B1F-2F),南棟 (1F-3F) の 3 つの建物である.これらの建物は互いに隣接しており,中庭や地下通路を通じて行き来できる (図1).被験者は Android 端末 2 台 (Nexus 4,Garaxy S3) を装着して建物内の様々な経路を移動する.この際被験者には普段と同様の歩行を心がけてもらい,通路中央の歩行,ドアを自分自身で開ける,階段を 1 段ずつ昇降するよう指示している.センサデータ収集には HASC Logger を用いた.収集したセンサ情報は,加速度 (3 軸),角速度 (3 軸),地磁気 (3 軸),気圧,無線 LAN の計 5 種類である.無線 LAN のサンプリングレートは 1Hz,その他は 100Hz とした.経路移動のセンサデータに対して,右左折や階段昇降といった詳細な行動ラベルが人手により付与されている.移動開始・終了,右左折やドア通過,階段昇降の開始・終了などのイベントに対しては,その行動をした時刻,行動の種類,対応するノード ID がラベルとして付与されている.また,イベントとイベントの間の移動行動 (リンクに相当) に対しては,行動の開始・終了時刻,行動の種類 (歩行・階段昇降),移動距離がラベルとして付与される.行動距離は,階段等の上下移動を伴う移動の場合,水平方向の移動距離に加え,垂直方向の移動距離,階段の段数が記述される.ラベルデータから経路を可視化した例を図 2 に示す.また各歩行移動データには,スタート地点を原点とした 1 秒ごとの 3 次元座標の正解データも用意されている.このデータを利用して,各研究で提案された歩行者デッドレコニング手法の精度を検証可能である.また,建物全体の歩行空間ネットワーク構造 (図 3) も用意されており,複数の歩行センシングデータを統合して建物構造を自動推定する研究のリファレンスデータとして利用できる.主な統計情報を表 1 に示す.被験者総数は 107 名,延べ経路センシングデータ数は 452 である.経路の種類は 114 種類であり,このうち同一建物内移動経路を 56 種類,異なる建物間の移動経路を 58 種類である.これまでの HASC Corpus では,被験者の年齢・性別的な偏りがみられていた.年齢や性別の違いは行動の仕方に影響を与える.例えば 20 代男性と 60 代女性では,歩行や階段昇降の姿勢や移動速度は明らかに異なる.そこで,HASC-IPSC ではできるだけ年齢と性別をバランスさせ,男女それぞれにつき 5 歳ごとの各世代の被験者数を 5 名程度用意した (表 2).本コーパスは,利用規約に同意いただければ,誰でも無料で利用可能である (http://hasc.jp/ipsc/).HASC-IPSC が本研究分野の発展促進に寄与することを期待する.

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Journal of the Institute of Electronics, Information and Communication Engineers  

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Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

近年モバイルコンピューティング分野で盛んな研究テーマとして,屋内位置推定や建物構造の自動推定が挙げられる.しかし,現在までにこれらの研究で共通して利用可能なデータが存在せず,新たな手法を提案しても,既存研究との精度の比較検討が困難であった.我々は,屋内位置推定研究と建物構造推定研究を主なターゲットとした,屋内歩行センシングコーパス(GASC-IPSC)を構築した.被験者はスマートフォンを装着して建物内の様々な経路を移動する.スマートフォンに記録された加速度等のセンサデータに対して,右左折や階段昇降といった詳細な行動ラベルが人手で付与されている.また各歩行移動データには,スタート地点を原点とした1秒ごとの3次元座標の正解データも用意されている.このデータを利用して,各研究で提案された歩行者デッドレコニング手法の精度を検証可能である.また建物全体の歩行空間ネットワーク構造の正解データが用意されており,複数の歩行センシングデータを統合して建物構造を自動推定する研究のリファレンスデータとして利用できる.

Language：English   Publishing type：Research paper (scientific journal)  

近年モバイルコンピューティング分野で盛んな研究テーマとして,屋内位置推定や建物構造の自動推定が挙げられる.しかし,現在までにこれらの研究で共通して利用可能なデータが存在せず,新たな手法を提案しても,既存研究との精度の比較検討が困難であった.我々は,屋内位置推定研究と建物構造推定研究を主なターゲットとした,屋内歩行センシングコーパス (HASC-IPSC) を構築した.被験者はスマートフォンを装着して建物内の様々な経路を移動する.スマートフォンに記録された加速度等のセンサデータに対して,右左折や階段昇降といった詳細な行動ラベルが人手で付与されている.また各歩行移動データには,スタート地点を原点とした 1 秒ごとの 3 次元座標の正解データも用意されている.このデータを利用して,各研究で提案された歩行者デッドレコニング手法の精度を検証可能である.また建物全体の歩行空間ネットワーク構造の正解データが用意されており,複数の歩行センシングデータを統合して建物構造を自動推定する研究のリファレンスデータとして利用できる.

Publishing type：Research paper (scientific journal)   Publisher：一般社団法人情報処理学会  

近年モバイルコンピューティング分野で盛んな研究テーマとして,屋内位置推定や建物構造の自動推定が挙げられる.しかし,現在までにこれらの研究で共通して利用可能なデータが存在せず,新たな手法を提案しても,既存研究との精度の比較検討が困難であった.我々は,屋内位置推定研究と建物構造推定研究を主なターゲットとした,屋内歩行センシングコーパス (HASC-IPSC) を構築した.被験者はスマートフォンを装着して建物内の様々な経路を移動する.スマートフォンに記録された加速度等のセンサデータに対して,右左折や階段昇降といった詳細な行動ラベルが人手で付与されている.また各歩行移動データには,スタート地点を原点とした 1 秒ごとの 3 次元座標の正解データも用意されている.このデータを利用して,各研究で提案された歩行者デッドレコニング手法の精度を検証可能である.また建物全体の歩行空間ネットワーク構造の正解データが用意されており,複数の歩行センシングデータを統合して建物構造を自動推定する研究のリファレンスデータとして利用できる.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

An indoor positioning method is required for the realization of life-log, navigation and so on, because the accuracy of GPS is generally not enough in indoor environments such as commercial facilities and stations. As a indoor positioning method, pedestrian dead reckoning and using the WiFi radio waves have been studied. In this paper, we propose an indoor positioning method using residual magnetism in addition to the above described methods. Moreover, we evaluated this method in real environment and confirmed that this method can provide accurate indoor positioning with an error of mean less than 4m.

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan (IPSJ)  

We propose a gate passing detection method based on WiFi signal strength and accelerometer of the user&#039;s smartphone. Gate such as door and elevater devides physical areas such as room and hallway. Thus, the WiFi environments of them tend to vary on a large scale. Conversely, it can be said that a gate should exist when the point that the WiFi environment varies on a large scale. We define such point &quot;WiFi Significant Point.&quot; We propose the detection method based on WiFi distance function and estimated moving distance according to accelerometer. We evaluated the proposal method and found out that more than half part of gate passing can be detected. We also found that the existence of gates that can be estimated same gate passing with a high degree of accuracy.

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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DOI： 10.1109/COMPSACW.2014.96

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electrical Engineers of Japan  

DOI： 10.1541/ieejjournal.134.832

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Other Link： https://jlc.jst.go.jp/DN/JALC/10041810063?from=CiNii

Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In recent years, along with the spread of WiFi-based location estimation service, the privacy problem has been pointed out. For existing WiFi-based location estimation service, users' traces and position information can be obtained fraudulently. Personal privacy may be compromised. This research proposes an algorithm that determines the reliability of the user by considering the probability of both spatial and temporal to resolve the privacy problem in WiFi-based location estimation. Meanwhile, we are operating a portal Locky. jp on location estimation using WiFi. Based on the actual data of base stations stored in the Locky. jp database, we confirm this algorithm. As a result, the percentage of returning position for privacy invasion data and whole data (privacy invasion data is also included) are about 20% and 55% respectively. In summary, we know that this algorithm reduced the privacy invasion to about 20% in the WiFi-based location estimation system.

DOI： 10.1109/ICMU.2014.6799059

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Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

Recent advancement of technology enables installations of small sized accelerometers or gyroscopes on various kinds of wearable/portable information devices. By using such wearable sensors, these devices can estimate its posture or status. However, most of current devices only utilize these sensors for simple orientation and gesture recognition. More deep understandings and recognition of human activity through these sensors will enable the next-generation human-oriented computing. To enable the real-world application by these kinds of wearable sensors, a large scale human activity sensing corpus might play an important role. Additionally, we have now a lot of high-performance mobile devices in real-world such as smart-phones. It is a great challenge to utilize such an enormous number of wearable sensors to collect a large-scale activity corpus. In recent years, there are several ongoing projects which are collecting human activities. Following on a huge success of last year's workshop, we are further planning to share these experiences of current research on human activity corpus and its applications among the researchers and the practitioners and to have a deep discussion for future of activity sensing.

DOI： 10.1145/2638728.2641314

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2014ap.html#conf/huc/KawaguchiINPR14

Language：Japanese   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery, Inc  

This research addresses improvement of the accuracy of pedestrian dead reckoning (PDR), which is one effective technique to estimate indoor positions using smartphone sensors. Even though various techniques using step lengths and their number have been previously proposed for PDR, insufficient accuracy is gotten from smartphone sensors. In this research, we define human activity sensing knowledge and propose improvements to PDR accuracy based on it. Human activity sensing knowledge consists of four kinds of information: pedestrian, environmental, activity, and terminal. Previous studies separately used these kinds of information
however, no study has systematically arranged them for use in PDR. We improved PDR accuracy by adjusting the step length in passages and on stairs and revised activity recognition error with human activity sensing knowledge. To investigate the effectiveness of that strategy, we used HASC-IPSC, which is an indoor pedestrian sensing corpus. After our investigation, activity recognition accuracy improved from 71.2% to 91.4%, and the distance estimation error was reduced from approximately 27 m to approximately 7 m using human activity sensing knowledge. Copyright 2014 ACM.

DOI： 10.1145/2638728.2641305

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2014ap.html#conf/huc/MurataHKK14

Publishing type：Research paper (scientific journal)  

Web of Science

Language：English   Publishing type：Research paper (scientific journal)  

CiNii Research

Language：English   Publishing type：Research paper (scientific journal)  

CiNii Research

Language：English   Publishing type：Research paper (scientific journal)  

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Lecture Notes in Engineering and Computer Science  

We propose a gate-passing detection method based on WiFi signal strength and the accelerometers of user smartphones. Since doors divide such physical areas as rooms and hallways, the WiFi environments tend to greatly vary. A gate should exist when the points in the WiFi environments vary on a large scale. We define such points as WiFi significant points and propose a detection method based on a WiFi distance function and estimated moving distance according to an accelerometer. We evaluated our proposed method and found out that most door passings can be detected. We also found that we can estimate the existence of doors that have identical door passings with a high degree of accuracy.

Scopus

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Authorship：Lead author   Language：Japanese  

音声や画像といった分野では，大規模データの活用により性能向上が図られてきたが，行動認識には利用できるデータが存在していない．我々は人間行動センシングコンソーシアム（Human Activity Sensing Consortium: HASC）を設立し，行動認識技術のより一層の進展のために大規模な行動センシングデータベース HASC corpusを構築した．HASC Challenge を2010年から3年間開催し，300名を超える被験者の行動データを収集した．データの応用例として被験者数の増大に基づく認識率向上や，経路識別を示す．また，年齢にバランスしたデータや，屋内案内をターゲットとしたデータ収集も進めている．これらのデータの多くは登録すれば無償で利用できるため，ぜひ利用して，あらたな成果を生んでいただきたい．

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DOI： 10.1109/COMPSACW.2013.97

Web of Science

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information and Media Technologies Editorial Board  

Explicit multiunicast (XCAST) has been proposed as a multicasting scheme with complementary scaling properties which can solve the scalability problems of conventional IP Multicast. XCAST is suitable for videoconferencing, online games and IPTV. This paper deals with QoS provisioning in XCAST networks using Differentiated Services (DiffServ). We show that integration of DiffServ in XCAST is a non-trivial problem due to inherent architectural differences between XCAST and DiffServ. We then propose a scheme called QS-XCAST that uses dynamic DSCPs to adapt to the heterogeneity of receivers in an XCAST network. We also provide an algorithm for harmonizing the <i>receiver-driven</i> and <i>sender-driven</i> QoS approaches between XCAST and DiffServ thereby determining the correct DSCP-PHB for all links in an XCAST network. By simulating using OMNeT++ we evaluate QS-XCAST using four metrics: throughput, average per-hop-delay, link utilization and forwarding fairness to other traffic in the network. Our solution eliminates <i>DSCP confusion</i> and <i>collusion attack</i> problems to which naive XCAST QoS provisioning is vulnerable. It also offers a more efficient bandwidth utilization, better forwarding fairness and less traffic load compared to the existing XCAST.

DOI： 10.11185/imt.8.182

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Recent advancement of technology enables installations of small sized accelerometers or gyroscopes on various kinds of wearable/portable information devices. By using such wearable sensors, these devices can estimate its posture or status. However, most of current devices only utilize these sensors for simple orientation and gesture recognition. More deep understandings and recognition of human activity through these sensors will enable the next-generation human-oriented computing. To enable the real-world application by these kinds of wearable sensors, a large scale human activity sensing corpus might play an important role. Additionally, we have now a lot of high-performance mobile devices in real-world such as smart-phones. It is a great challenge to utilize such an enormous number of wearable sensors to collect a large-scale activity corpus. In recent years, there are several ongoing projects which are collecting human activities. In this workshop, we are planning to share these experiences of current research on the human activity corpus and its applications among the researchers and the practitioners and to have a deep discussion for future of activity sensing. Copyright © 2013 ACM.

DOI： 10.1145/2494091.2495992

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2013ap.html#conf/huc/KawaguchiNRFP13

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：INT ASSOC ENGINEERS-IAENG  

We propose a gate-passing detection method based on WiFi signal strength and the accelerometers of user smart-phones. Since doors divide such physical areas as rooms and hallways, the WiFi environments tend to greatly vary. A gate should exist when the points in the WiFi environments vary on a large scale. We define such points as WiFi significant points and propose a detection method based on a WiFi distance function and estimated moving distance according to an accelerometer. We evaluated our proposed method and found out that most door passings can be detected. We also found that we can estimate the existence of doors that have identical door passings with a high degree of accuracy.

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Web of Science

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Up till now, the majority of researches related to location estimation and floor plan creation have used different kinds of data and there has simply been no technique to compare the relative advantages and disadvantages. We collected indoor pedestrian sensing data of 100 people with a balance of gender and age. The data is part of the HASC corpus, free to use for research purposes. Copyright © 2013 ACM.

DOI： 10.1145/2494091.2495981

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2013ap.html#conf/huc/KajiWBK13

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CiNii Research

Language：English  

DOI： http://dx.doi.org/10.1109/IPIN.2012.6418943

Language：English  

Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

人間行動の理解は，情報システムの究極の目標の一つである．我々は装着型センサを用いた人間行動の大規模コーパスの構築を行うために，HASC Challengeを2010年,2011年と開催した．これらの成果として，HASC2011corpus(被験者数116, 行動データ数4898),HASC2012corpus(被験者数136, 行動データ数7668)を構築した．本稿では，HASC2012corpusの概要と，新たな実環境データの応用について述べる．

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ICST/ACM  

DOI： 10.4108/icst.simutools.2012.247761

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Other Link： http://dl.acm.org/citation.cfm?id=2263067

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Recent advancement of communications systems enables that any person can easily connect to others using network devices. However, it is still bothering to setup an external device to communicate with other devices. This paper proposes a framework for external device session initiation using a mobile phone connection information. To overcome the complex configuration issues, we employ several global servers for session initiation. By using connection information of a mobile phone, one can easily connect an external device with other device without setting any connection information. By using the framework, we have developed a prototype which can easily connect Kiosk video terminal with other device just facing an Android smartphone with NFC feature over the Kiosk terminal. We have counted the steps required to the user for the session initiation, and confirm the framework overcomes the issues. Copyright 2012 ACM.

DOI： 10.1145/2402599.2402607

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Other Link： http://dblp.uni-trier.de/db/conf/aintec/aintec2012.html#conf/aintec/AsaiKK12

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Up to now Scene Analysis has been based on the WiFi location estimation technique and it has been necessary to have a large scale database and a large amount of calculation. We propose a WiFi estimation method that uses little data or calculation. First of all we apply Gaussian Mixture Model to represent the large scale WiFi database to decrease the WiFi data by no less than 95%. Secondly, we apply Particle Filter to adjust the possible calculation quantity needed for the location estimation technique. As experimental result, we achieved real-time location estimation within 6 similar to 10m. Another important issue for Scene Analysis technique is the high cost of operation of the previous WiFi observation. Accordingly crowdsourcing approach was used, employing as system where some users could contribute and other uses could share. The ideal system is a composition of the Web and mobile terminal. WiFi data observed by mobile terminals is uploaded to a Web server where it is managed and integrated into GMM and large scale operations are carried out on data and calculations. When the lightweight modeled data is downloaded to the mobile-terminal, the mobile terminal then has the ability to carry out real-time location estimation independently.

DOI： 10.1109/IPIN.2012.6418943

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モバイル端末向けに提供されるWeb サービスやアプリケーションなどのサービス
の数は爆発的に増加しており，膨大な数のサービスの中から自分に必要な物を探し出
すことが困難となりつつある．我々は，ユーザの状況に応じてサービスの推薦を行う
システムの実現を目指す．推薦を実現する上で必要となる学習情報の獲得を行うため
に，現在状況をクエリとするアプリ検索サービスを提供し，ユーザ状況の収集から推
薦までを行うプラットフォームを提案する．提案に基づいた状況依存型アプリ検索シ
ステム「App.Locky」の実装を行い，インターネット上にて公開した上で，実ユーザ
を対象とした大規模検索ログ収集実験を行った．実験の結果，収集された検索ログを
ユーザ状況の推定に利用可能であることを確認した．

Language：Japanese   Publishing type：Research paper (scientific journal)  

我々は，様々な建物において屋内位置情報を一般に利用できる環境の実現を目指し，
GMM に基づく無線LAN 位置推定技術を用いた屋内位置情報基盤indoor.Locky の
研究開発を行っている．本基盤の実用化に向けた最も大きな問題は， 一般ユーザが行
う事前の無線LAN 観測の作業コストが高いという点である．そこで，この作業を複
数ユーザで分担できるようUGC(User Generated Content) のアプローチを採用す
る．商業施設に置いて実証実験を行い，複数ユーザの観測情報がWeb サービス側で
正常に統合されることを確認した．また最も作業コストと位置推定精度のバランスが
とれた観測地点の間隔が5m 程度であることを明らかにした．これにより，一般ユー
ザが行う観測作業の適切な間隔を提示可能になるため，多くの箇所の観測を行ってし
まい作業コストが必要以上増加してしまうのを防ぐことができる．

Language：English   Publishing type：Research paper (scientific journal)  

Currently, there are many indoor positioning researches using wireless LAN etc. However, there is no wide existance of structural map information on buildings or underground centers that can be applied in indoor positioning. In order to implement indoor location-based services such navigation based on user location, indoor map information is required. Moreover, indoor map information is also useful in the development of research in indoor positioning. This research aims at widely collecting indoor structure map information based on floor map pictures from sources such websites and a the guidance information for an institution through crowdsourcing approach. This paper describes a series of flows, such as acquisition of a floor map, automatic structuring, modification of users, concentration of structural information, and sharing of structural information. It also describes the system which used in supporting this approach. In addition, details of the contents of structural information are also given.

Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

We have operated a portai Locky.jp on location information and estimation using wireless LAN. It is possible to estimate the current location based on the database of wireless LAN information observed and submitted by general users. However, the failure in wireless LAN observation, together with the base station which has been moved, base station in Shinkansen, and mobile WiFi, would cause adverse effect in accuracy of location estimation if they did not have the proper handle. In this paper, intended with the large base station database collected by Locky.jp, we detect the failure in wireless LAN observation and base station which has been moved, and fix them to maintain the integrity of wireless LAN database. The results of evaluation confirmed the improvement of the location estimation accuracy with applying these corrections.

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Locky.jp project handles more than 1 million WiFi AP information which is extracted from 100 million crowd sourced data. Our smart phone applications which include "Eki.Locky" are downloaded over 1 million times. By serving such services, we get huge amount of usage data. There are several frameworks to handle this kind of huge information. However, there is no mature technology to handle them. In this talk, I'll introduce our case-study and current study about heterogeneous data handling.

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In this paper we therefore propose an "XCAST6 Routing Engine", an out-of-the-box solution that simplifies gradual deployment of XCAST in the real-world. The contribution of this paper is two fold. We not only provide a simple solution that can hasten deployment of XCAST on the real Internet but we also exemplify through experimental performance evaluation of our
solution with respect to a number of performance and resource utilization metrics, that contrary to other perceptions, XCAST does not actually add an extra ordinary load to the routing resources.

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Recently, demand of presentation video is increasing so that various conferences provide the streaming service of presentation video. In many cases, presentation slides and posters in the streaming video are not so visible because of various reasons such as low resolution and distortion. Users cannot understand the presentation clearly in such situation. In this paper, we propose a streaming video augmentation method to improve the visibility of the content in the streaming video. In the method, the client downloads the original data of the content in the streaming video previously. Then, in the watching phase, the image generated from original data is superimposed at the location of the content area in the streaming video. We implemented poster presentation live streaming system. We also conducted experiment of the system. In result, we found out that the system improve the visibility of poster in the streaming video, and reduce the flustration of watching the video.

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 1069-1081

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named "HASC Challenge" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named "HASC Tool" for management, evaluation and collection of the large number of activity sensor data. Copyright 2011 ACM.

DOI： 10.1145/1959826.1959853

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Other Link： http://dblp.uni-trier.de/db/conf/aughuman/aughuman2011.html#conf/aughuman/KawaguchiOIKTMIKSN11

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Web of Science

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Web of Science

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Explicit multiunicast (XCAST) is a form of small group multicast in which the sender embeds IP addresses of all receiving nodes in a special header and sends it with all data packets. On sending, each XCAST-aware router in the path examines the packet and partitions the destinations into various sets based on their next hops. For each set, the router uses a bitmap in the packet header to ensure that each copy of the packet is delivered only to destinations whose corresponding bits in the bitmap are set to 1. Sometimes real world implementation of XCAST6 can take a long time to deploy and resource constraints can limit the scale of testing that can be done. We have therefore integrated XCAST6 into the IPv6 stack of INET Framework for OMNeT++ to allow for large-scale, exhaustive and realistic investigation of XCAST6 characteristics and to complement our current research into real world deployment of XCAST6. The contribution of this paper is two fold. First, it describes our implementation of XCAST6 in OMNeT++'s INET framework. Secondly, it experimentally shows the effectiveness of XCAST6 with regards to various multicast performance metrics such as stress, end-to-end delay, efficiency and packet processing overhead rate.

DOI： 10.1145/2089016.2089021

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Other Link： http://dblp.uni-trier.de/db/conf/aintec/aintec2011.html#conf/aintec/AbadeKK11

Language：English   Publishing type：Research paper (international conference proceedings)  

Recently, it has become very hard for users to find their desired mobile services because the number of applications and Web services are rapidly increasing. Therefore, it is important to realize context-aware application recommendation. Because it is necessary to collect large learning data to estimate user's context, we propose a platform for collecting users' context and relationship between context and application by providing an application search system that inquires user's current context. We implemented a system named "App.Locky" based on our proposal and conducted experiments by publishing the system on the internet. As a result, we confirmed that collected search logs can be used to estimate user's context and relationship between context and application. © 2011 ACM.

DOI： 10.1145/2030100.2030110

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

To accelerate and simplify human activity recognition research, we have been developing a data processing tool named "HASC Tool." As the activity corpus becomes huge, it is not simple to handle the large number of files because it takes a lot of time to process. In this paper, we propose a distributed data processing mechanism which is implemented in the HASC Tool. By using the system, we can simply scale the local system into distributed processing. We also show the preliminary experimental result. © 2011 Authors.

DOI： 10.1145/2030112.2030234

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2011.html#conf/huc/KawaguchiOIK11

Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus. © 2011 Authors.

DOI： 10.1145/2030112.2030218

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Other Link： http://dblp.uni-trier.de/db/conf/huc/ubicomp2011.html#conf/huc/KawaguchiYYOIKTMIKSN11

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CiNii Research

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CiNii Research

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

We introduce an iPhone / iPod touch timetable application using WiFi location system named "Eki.Locky". This application adopts UGC (User Generated Content) approach to collect TimeTable information andWiFi access point (AP) information from public users. Since the service started in October 2009, Eki.Locky has been used by over 440,000 people, posted timetable information covers 98% of all stations in Japan and 350,000 WiFi AP information were collected. In addition, since June 2010, we started a new version of this application named "TimeTable.Locky" which supports any timetable such as buses and airplanes. TimeTable.Locky also has been used large number of people, and collected over 23,000 of timetable information. Copyright 2010 ACM.

DOI： 10.1145/1899662.1899669

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Other Link： http://dblp.uni-trier.de/db/conf/locweb/locweb2010.html#conf/locweb/YanoKK10

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DOI： 10.1007/978-0-387-79582-9_6

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