Publication

@inproceedings{Chenwei2020_LOIS,

title = {Mobile Sensing of Pedestrian Mobility and its Assessment},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.ieice.org/ken/paper/20200312h1vo/eng/},

isbn = {0913-5685},

year  = {2020},

date = {2020-03-04},

booktitle = {IEICE Tech. Rep.},

volume = {119},

number = {477},

pages = {121--126},

publisher = {IEICE Technical Committee},

organization = {Life Intelligence and Office Information Systems (LOIS)},

abstract = {We propose a client-server system that provides crowd detection and mobility information. Our proposed system has the advantages of low cost and location flexibility without pre-deployed, as long as there is a sufficient number of users involved. We conducted several experiments in real environments to determine the feasibility, accuracy and applicable environment of the system. The result shows that the system can effectively capture the flow of people in the experimental area. In some cases, under the same environment, it can obtain almost the same mobility tracking information from fewer participating users than the GPS method.

},

keywords = {Bluetooth, Crowd detection, Human mobility},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3356995.3364541,

title = {Using Mobile Sensing Technology for Capturing People Mobility Information},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3356995.3364541},

doi = {10.1145/3356995.3364541},

isbn = {9781450369640},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility},

pages = {33–40},

publisher = {Association for Computing Machinery},

address = {Chicago, IL, USA},

series = {PredictGIS’19},

keywords = {Bluetooth, Crowd detection, Human mobility},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kitazato2018study,

title = {A Study of the Detection of Pedestrian Flow Using Bluetooth Low Energy},

author = {Tomoya Kitazato and Masaki Ito and Kaoru Sezaki},

doi = {10.1109/PERCOMW.2018.8480336},

year  = {2018},

date = {2018-10-08},

booktitle = {2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},

pages = {716--721},

publisher = {IEEE},

address = {Athens, Greece},

organization = {IEEE},

abstract = {Analysis of human mobility data provide us many important insights. For example, a detailed analysis of human mobility in urban space can provide important insights into gathering events. This insight is useful when addressing urban planning and public safety issues, and serves as a powerful tool for solving traffic congestion, early detection of social unrest, and so on. The analysis of human mobility in an indoor space such as in a museum exhibition, can assist us in anticipating the behavior of visitors; and in the early recognition of potential problems, such as the buildup of foot traffic at specific points. However, there is no universally accepted method for easily sensing human mobility. To address this problem, we developed a novel method to detect pedestrian flow using Bluetooth Low Energy (BLE). Our approach is based on the assumption that a BLE beacon is always affixed to the pedestrian. Thus, the individual's velocity can be readily determined by analyzing the Received Signal Strength Indicator (RSSI) of their BLE beacon. Apart from velocity, the direction of the pedestrian can also be determined by detecting their BLE beacon with multiple sensors. In this investigation, we evaluated the proposed method using both experimental real-world data and simulations. Participants with BLE beacons walked straight in a hall while the RSSI of their beacons was monitored from a moving sensor. This information was used to estimate the velocities of the beacons. We also simulated the RSSIs of the beacons and estimated their velocities under various conditions. Our results indicate that the proposed method can precisely detect the velocities of pedestrians.},

keywords = {Bluetooth, Directive antennas, Global Positioning System, Laser radar, Proposals, sensors, Wireless fidelity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{北里知也2017クラウドセンシングによる,

title = {クラウドセンシングによる Bluetooth を用いた人流把握の検討},

author = {北里知也 and 伊藤昌毅 and 瀬崎薫},

url = {https://www.ieice.org/ken/paper/20170721xbVm/},

year  = {2017},

date = {2017-07-12},

booktitle = {知的環境とセンサネットワーク（ASN）研究会},

journal = {電子情報通信学会技術研究報告= IEICE technical report: 信学技報},

volume = {117},

number = {134},

pages = {193--198},

publisher = {電子情報通信学会},

abstract = {人々の移動軌跡を記録したデータを分析することで，美術館や博物館における来場者の行動パターンを分析したり，都市機能の分析をしたりすることができる．

しかし，人流を低コストでセンシングできる実用的な手法はない．

そこで，今回Bluetoothを用いた低コストな人流把握手法を提案する．

提案手法では，人流の大きさと方向のそれぞれを別々に取得する．

さらに，提案手法の実現可能性を確かめる2つの実験を行った．

1つ目の実験では，人流の大きさをセンシングする手法の実現可能性を確かめられた．

2つ目の実験では，人流の移動方向をセンシングする手法の実現可能性を確かめられた． 

By analyzing human mobility data, we can know the activity patterns of a museum visitor, functional compositions of urban spaces, and so on.

However, there is no low-cost way to sensing human mobility easily.

Therefore, we propose the low-cost sensing system for recognizing human mobility using Bluetooth.

Our system obtains a size and a direction of human mobility separately.

Moreover, we show the feasibility of our proposal by two experiments.

The first one demonstrates the feasibility of detecting a size of human mobility.

The second one shows the feasibility of obtaining a direction of human mobility.},

keywords = {Bluetooth, Congestion sensing, crowd sensing, Human mobility flow},

pubstate = {published},

tppubtype = {inproceedings}

}