出版者・発行元：2nd ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge, HuMob-Challenge 2024  

Human Mobility Prediction Challenge 2024 was organized to compare human future movement prediction methods using a unified dataset. The challenge focuses on human movement prediction in multiple cities with varying numbers of users. Many existing movement prediction methods train deep learning models using large-scale movement histories from cities and make predictions. While cities with large users have sufficient training data, smaller cities with fewer users may face challenges due to insufficient data, raising concerns about lower prediction accuracy. Additionally, it is difficult to treat stay locations between different cities’ movement histories due to differences in spatial area arrangement, making it challenging to share data across cities. To address this issue, we propose human movement prediction method that utilizes time-series stay frequency patterns, which can be commonly applied across different cities. This method demonstrated superiority in predicting movements in cities with fewer users compared to models trained and predicted using only the movement histories of the target city. Furthermore, the method achieved top 10 prediction accuracy in HuMob Challenge 2024.

DOI： 10.1145/3681771.3699909

Scopus

出版者・発行元：32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024  

Understanding the characteristics of various urban areas is crucial for applications such as urban planning, tourism policies, market analysis, and infection control. Techniques for embedding areas as vectors in a latent space based on human mobility patterns are actively researched. Many of these area embedding methods define areas as points, grids, or polygons on a geospatial plane and then embed them. However, existing methods do not allow for mutual transformation between these forms and sizes after the initial embedding. Additionally, if the characteristics of an area change due to events such as the opening of new buildings, re-embedding is necessary. Meanwhile, the Word2Vec technique, a representative word embedding method, has a property called additive compositionality. This property allows for the arithmetic operation of word meanings through the arithmetic operations of word embeddings. In this paper, we propose a method to apply this property to existing area embedding techniques, leveraging it for practical tasks such as area shape transformation and searching for areas with trends change.

DOI： 10.1145/3678717.3691279

Scopus

出版者・発行元：32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024  

Clustering people by their life patterns is valuable in government and business fields. Existing studies often rely on semantic data such as Point of Interest or stay purpose. However, they have the problem that obtaining large datasets is difficult due to the need for annotation work. Some studies try to use only location data. However, they do not reveal the semantics of the area where visitors stay because they only label visited areas by significance according to duration and frequency of stay. In this paper, we propose a framework, LPSeL, for clustering people’s Life Patterns at a Semantic Level using only raw GPS location data. LPSeL is based on the idea that analyzing human mobility first requires understanding urban space. Therefore, it begins with area modeling, which models areas in a city based on people’s activities. Then, treating human mobility as a sequence of area representations makes it possible to model individuals by semantic-level characteristics of their life patterns. We showed that LPSeL is capable of estimating people’s attributes from their life patterns using a real-world dataset consisting of GPS data collected from tens of thousands of smartphone users.

DOI： 10.1145/3678717.3691309

Scopus

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

DOI： 10.20729/00239895

出版者・発行元：UbiComp Companion 2024 - Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing  

In recent years, advancements in telepresence technology have made remote communication and collaboration easier. MetaPo is a system that uses spherical displays to seamlessly integrate physical and digital interactions. However, conventional inverse panorama techniques face challenges such as image seams, coverage issues, and overlapping displays. To address these challenges, this study proposes an inverse panorama technique based on coordinate transformation, enhancing visual continuity and user experience. Additionally, evaluation methods necessary to demonstrate the effectiveness of the proposed method in improving the quality of remote communication have been devised. This research provides a novel solution to challenges in spherical display technology and paves the way for more immersive telepresence experiences.

DOI： 10.1145/3675094.3678369

Web of Science

Scopus

出版者・発行元：UbiComp Companion 2024 - Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing  

Although object detection technology using cameras offers potential for various applications, it incurs dataset creation costs to train new models where general-purpose models are ineffective, such as in industrial settings. We have previously developed a semi-automated annotation framework that employs optical flow and representation learning techniques to reduce human effort significantly. However, it was likely to cause unintended annotation omissions and mistakes compared to manual annotation. In this study, we propose a composite image generation approach to create omission-free and pattern-rich datasets. The proposed method synthesizes natural-looking images without unannotated targets by placing labeled foreground segments at their original positions on targetless background frames collected with the same fixed-point cameras. Evaluation with video footage in a logistics warehouse confirmed that improved dataset reliability led to higher model performance.

DOI： 10.1145/3675094.3678447

Web of Science

Scopus

出版者・発行元：UbiComp Companion 2024 - Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing  

Although VR headsets have gained widespread acceptance, their potential for real-world actuation remains underexplored. We propose a system that allows to work in VR space using a robotic arm and VR headset, focusing on 3D scanning as the task. The robotic arm is equipped with an RGB-D camera to achieve remote 3D scanning. This system enables immersive remote work and enhances the link between real space and VR space by reproducing the real objects into the VR space.

DOI： 10.1145/3675094.3677548

Web of Science

Scopus

出版者・発行元：UbiComp Companion 2024 - Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing  

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require a large-scale human activity corpus and much-improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Fifth Sussex-Huawei Locomotion and Transportation Recognition Challenge and the First WEAR Dataset Challenge in special sessions.

DOI： 10.1145/3675094.3677561

Web of Science

Scopus

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

担当区分：筆頭著者   記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

DOI： 10.20729/00234831

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

DOI： 10.20729/00234833

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

DOI： 10.20729/00234832

出版者・発行元：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

Web of Science

Scopus

出版者・発行元：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

Web of Science

Scopus

出版者・発行元：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

Web of Science

Scopus

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 情報処理学会  

出版者・発行元：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

Web of Science

Scopus

出版者・発行元：HuMob 2023 - 1st ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge  

Understanding, modeling, and predicting human mobility patterns in urban areas has become a crucial task from the perspectives of traffic modeling, disaster risk management, urban planning, and more. HuMob Challenge 2023 aims to predict future movement trajectories based on the past movement trajectories of 100,000 users[1]. Our team, "uclab2023", considered that model design significantly impacts training and prediction times in the task of human mobility trajectory prediction. To address this, we proposed a model based on BERT, commonly used in natural language processing, which allows parallel predictions, thus reducing both training and prediction times.In this challenge, Task 1 involves predicting the 15-day daily mobility trajectories of target users using the movement trajectories of 100,000 users. Task 2 focuses on predicting the 15-day emergency mobility trajectories of target users with data from 25,000 users. Our team achieved accuracy scores of GEOBLEU: 0.3440 and DTW: 29.9633 for Task 1 and GEOBLEU: 0.2239 and DTW: 44.7742 for Task 2[2][3].

DOI： 10.1145/3615894.3628498

Scopus

出版者・発行元：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

Scopus

出版者・発行元：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

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： https://doi.org/10.1109/ICAIIC57133.2023.10067079

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

The COVID-19 pandemic, which began in 2020, has changed people’s lives, and people are shopping more online. While the analysis of online shopping is becoming increasingly important, regional differences in consumption trends exist. This study proposes a data-driven regional modeling method based on EC purchase data to examine the regional characteristics of online shopping purchase trends. Using the proposed method, we quantified and visualized the degree of similarity of consumption trends among regions and the degree of dispersion of consumption trends within regions using approximately 300,000 lines of online shopping history for 3 years in Japan. As a result, we found that there was some disruption in the early stages of e-commerce for food products by region, while there was little difference in consumption trends among regions for daily necessities. In addition, for consumer durables and clothing, regional differences in consumption trends were confirmed in terms of the number of cars owned per capita, urbanization status, and other regional characteristics.

DOI： 10.1007/978-3-031-34668-2_26

Web of Science

Scopus

出版者・発行元：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

Scopus

出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

This study explores the feasibility of dialogue systems with individuality capable of providing continuous and lasting assistance via a multiple device dialogue system. A framework has been devised to manage dialogue history, allowing for the use of a singular identity across various interfaces, including chatbots and virtual avatars. This framework can summarize and save the dialogue history, which can be utilized to generate responses. The impact of dialogue history sharing on users’ interactions with a particular character across various devices was assessed for naturalness, continuity, and reliability. The results indicate that dialogue history sharing can foster more natural and continuous conversations, thereby enhancing the potential for long-term support. This research advances the proposition that a digital agent endowed with a consistent identity across multiple devices can provide personalized and sustained support to users.

DOI： 10.1007/978-3-031-35602-5_25

Web of Science

Scopus

Web of Science

出版者・発行元：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

Scopus

出版者・発行元：UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing  

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require a large-scale human activity corpus and much-improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Fifth Sussex-Huawei Locomotion and Transportation Recognition Challenge in a special session.

DOI： 10.1145/3594739.3605106

Web of Science

Scopus

Web of Science

出版者・発行元：UbiComp/ISWC 2022 Adjunct - Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers  

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition.

DOI： 10.1145/3544793.3560377

Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： https://doi.org/10.1007/978-3-031-05463-1_21

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IEEE Sensors Journal  

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-The-Art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

DOI： 10.1109/JSEN.2021.3083149

Open Access

Web of Science

Scopus

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）   出版者・発行元：一般社団法人 人工知能学会  

DOI： 10.11517/jsaikbs.125.0_03

CiNii Research

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：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

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：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

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：ACM International Conference Proceeding Series  

With the introduction of the IoT, communication is becoming more diverse. While IoT is becoming more and more widespread, there are some forms of human communication are being lost. The development of self-propelled robots has progressed, and electric and automated wheelchairs are also being utilized. A caregiver was originally required to operate a wheelchair. However, with the advent of IoT wheelchairs, wheelchairs can be operated by wheelchair users only, increasing convenience. On the other hand, wheelchairs have been used as a kind of communication between caregivers and wheelchair users, and the development of IoT wheelchairs has reduced the opportunities for communication between caregivers and wheelchair users. In this paper, we propose a method to reproduce communication scenarios in the following two scenarios using an IoT wheelchair and present the design and implementation of the prototype created.

DOI： 10.1145/3567445.3571111

Web of Science

Scopus

出版者・発行元：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

Web of Science

Scopus

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

<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

Open Access

Scopus

CiNii Research

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers  

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Fourth Sussex-Huawei Locomotion and Transportation Recognition Challenge and the Third Nursing Activity Recognition Challenge in special sessions.

DOI： 10.1145/3460418.3479266

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：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

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：13th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2021  

The continuously changing conditions of cities are now technically understandable in the information space through real-world sensing methods and analytical methods such as big data analysis and machine learning. On the other hand, it is currently difficult to estimate and present what the people in the city need (latent demand). This paper aims to solve latent demand by developing Latent Demand Resolver (LD-Resolver), the new latent demand estimation and exchanging system. LD-Resolver has two components, Latent Demand Extractor (LD-Extractor) and Latent Demand Exchanger (LD-Exchanger). LD-Extractor extracts a latent demand from various social conditions using IoT sensors, Web, and SNS. LD-Exchanger has a new structure to exchange a latent demand with an appropriate service supply. Finally, we developed the LD-Resolver and conducted a demonstration experiment at the Higashiyama Zoo and Botanical Garden to verify the method. As a result of the two-week experiment, the proposed method can be effectively used in actual facility operations.

DOI： 10.23919/ICMU50196.2021.9638789

Scopus

記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：一般社団法人 情報処理学会  

本研究では社交ダンスを対象とし，加速度・角速度，視覚のマルチモーダルセンサを用いたダンスフィガー分類手法を提案する．社交ダンスにおける基本技術であるフィガーは，その種類の多さや複雑さゆえ初心者や中級者にとって練習が困難である．フィガーの自動認識によりユーザは自身の踊りの客観的な把握が可能となるため，ダンスフィガーの学習支援が可能となると考えられる．一方で，社交ダンスは2人1組で多彩な動きを行うため，既存の行動認識手法をそのまま適用することは難しい．本稿では，社交ダンスの動作特性を考慮し，フィガーの複雑さや遮蔽などの課題を解決したダンスフィガー認識手法を実現した．本研究では一般的な行動認識手法をベースライン手法として実装したうえで，社交ダンスの姿勢や動作特性を考慮した特徴量を設計・利用した手法を提案・実現し，両者の評価を行った．結果，提案手法の認識精度はF値0.97となり，ベースライン手法を全体で0.06上回った．特にフィガー別の分類精度では，最大で0.6の精度向上を達成した．さらに提案手法が遮蔽物に対しても頑健さを有することを示した．

計算機科学の基礎数学として，離散数学の基礎概念・基礎知識を学び，演習を通じて身につけることを目的とする．

集合論，整数論，代数系の基礎的な定義を理解し，種々の問題を解くことができる．

電気電子情報工学に関する以下のテーマについて実験・レポートの作成を行う。
実験を通して、線形回路論、電気回路論、電子回路工学、情報理論、電気磁気学、ディジタル回路に関する確かな知識を獲得するとともに、計画力、応用力、チームワーク能力が養成されることを目的とする。

C3B8 ディジタル信号処理
学生は、取り組んだテーマについて理解し、説明ができる。

電気電子情報工学に関する以下のテーマのうち１つについて、実験の計画案、実行、検討、結果の報告発表を行う。それぞれの自主性・独創性を期待する。
実験を通して、課題探求と問題解決の過程を体験し、そのテーマに関する確かな知識を獲得するとともに、計画力、応用力、チームワーク能力が養成されることを目的とする。

学生は、取り組んだテーマについて理解し、説明ができる。
EH15 「実社会データセンシング・分析・可視化」