研究者詳細 - 河口　信夫

「究極のヒューマンインタフェースの実現を目指して」

A Three-Dimensional Smartphone Positioning Method using a Spinning Magnet Marker

Watanabe Kosuke, Hiroi Kei, Kamiyama Takeshi, Sano Hiroyuki, Tsukamoto Masakatsu, Katagiri Masaji, Ikeda Daizo, Kaji Katsuhiko, Kawaguchi Nobuo

2017 TENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU) 頁： 38 - 44 2017年

河口信夫

知能と情報 28 巻 ( 6 ) 頁： 169 - 169 2016年12月

Wi-Fi Human Behavior Analysis and BLE Tag Localization: A Case Study at an Underground Shopping Mall 査読有り

Kawaguchi N., Hiroi K., Shionozaki A., Asukai M., Nasu T., Hashimoto Y., Nakamura T., Gotou T., Ando S.

ACM International Conference Proceeding Series 28- 巻頁： 151 - 159 2016年11月

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

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

PIEM: Path Independent Evaluation Metric for Relative Localization 査読有り

Abe M., Kaji K., Hiroi K., Kawaguchi N.

2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016 頁： 1 - 8 2016年11月

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：2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016

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

Urban planning: Is a networked compact city vision realistic?

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data Open Access

Wang Weimin, Sakurada Ken, Kawaguchi Nobuo

REMOTE SENSING 8 巻 ( 11 ) 2016年11月

Kawaguchi N.

Labor Forces and Landscape Management: Japanese Case Studies 頁： 373-380 2016年1月

Estimating 3D Pedestrian Trajectories using Stability of Sensing Signal

Kaji Katsuhiko, Kawaguchi Nobuo

2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN) 頁： . 2016年

UbiComp/ISWC 2015 PDR Challenge Corpus 査読有り

Kaji Katsuhiko, Abe Masaaki, Wang Weimin, Hiroi Kei, Kawaguchi Nobuo

UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING 頁： 696 - 704 2016年

記述言語：日本語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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

Multi-Algorithm On-Site Evaluation System for PDR Challenge

Kaji Katsuhiko, Kanagu Kohei, Murao Kazuya, Nishio Nobuhiko, Urano Kenta, Iida Hirokazu, Kawaguchi Nobuo

2016 NINTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORKING (ICMU) 頁： 74-79 2016年

Measurement Methods of Spatial Ability using a Virtual Reality System

Shimada Kosuke, Hiroi Kei, Kawaguchi Nobuo, Kaji Katsuhiko

2016 NINTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORKING (ICMU) 頁： 80-85 2016年

4th Workshop on Human Activity Sensing Corpus and Applications: Towards Open-Ended Context Awareness 査読有り Open Access

Kawaguchi Nobuo, Nishio Nobuhiko, Roggen Daniel, Inoue Sozo, Pirttikangas Susanna, van Laerhoven Kristof

UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING 頁： 690 - 695 2016年

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

A Better Positioning with BLE Tag by RSSI Compensation through Crowd Density Estimation 査読有り

Hiroi Kei, Shinoda Yoichi, Kawaguchi Nobuo

UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING 頁： 831 - 840 2016年

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

A Location Estimation Method using BLE Tags Distributed Among Participants of a Large-Scale Exhibition 査読有り

Urano Kenta, Hiroi Kei, Kaji Katsuhiko, Kawaguchi Nobuo

ADJUNCT PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING NETWORKING AND SERVICES (MOBIQUITOUS 2016) 28-November-2016 巻頁： 124 - 129 2016年

記述言語：日本語掲載種別：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

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

FloodEye: Real-time Flash Flood Prediction System for Urban Complex Water Flow

Hiroi Kei, Kawaguchi Nobuo

2016 IEEE SENSORS 頁： . 2016年

HASC-PAC2016: Large Scale Human Pedestrian Activity Corpus and Its Baseline Recognition 査読有り

Ichino Haruyuki, Kaji Katsuhiko, Sakurada Ken, Hiroi Kei, Kawaguchi Nobuo

UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING 頁： 705 - 714 2016年

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

安定センシング区間検出に基づく3次元歩行軌跡推定手法査読有り Open Access

梶克彦, 河口信夫

情報処理学会論文誌 57 巻 ( 1 ) 頁： 12 - 24 2016年

Multi-algorithm on-site evaluation system for PDR challenge 査読有り

Kaji K., Kanagu K., Murao K., Nishio N., Urano K., Iida H., Kawaguchi N.

2016 9th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2016 頁： 74 - 79 2016年

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：2016 9th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2016

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

FloodEye: Real-time Flash Flood Prediction System for Urban Complex Water Flow 査読有り

Kei Hiroi, Nobuo Kawaguchi

2016 IEEE SENSORS 2016年

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：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.