2020年3月23-27日に、テキサスAustinで開催されるIEEE PerCom 2020内で開催される3つのワークショップで3件の発表を行います。
PerHealth 2020: 一般社員60名を対象としたストレスセンシングの研究
Yuri Tani; Shuichi Fukuda; Yuki Matsuda; Sozo Inoue; Yutaka Arakawa WorkerSense: Mobile Sensing Platform for Collecting Physiological, Mental, and Environmental State of Office Workers Inproceedings PerHealth 2020: 5th IEEE PerCom Workshop on Pervasive Health Technologies (PerHealth 2020), 2020. @inproceedings{Tani2003:WorkerSense, title = {WorkerSense: Mobile Sensing Platform for Collecting Physiological, Mental, and Environmental State of Office Workers}, author = {Yuri Tani and Shuichi Fukuda and Yuki Matsuda and Sozo Inoue and Yutaka Arakawa}, year = {2020}, date = {2020-03-23}, booktitle = {PerHealth 2020: 5th IEEE PerCom Workshop on Pervasive Health Technologies (PerHealth 2020)}, abstract = {In data collection of the human physiological and psychological conditions for mental healthcare (e.g., work engagement), measurement methods using environment-installed sensors and questionnaire surveys have been often used. However, these approaches are not practical in continuous data collection, due to the large burden for people. Recently, in association with advancing sensing technology with IoTs, sensing by small sensors and wearable devices has become possible easily. In this paper, we aim to establish a simple and general sensing method based on a mobile application for measuring physiological and psychological state of office workers and environmental state. Through the experiment for 2-3 weeks involving 60 office workers of four Japanese companies by using our application, we succeeded to create a dataset of physiological, environment, and mental state. This paper explains the developed mobile application, experimental procedure, and a summary of the data collected in the experiment.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In data collection of the human physiological and psychological conditions for mental healthcare (e.g., work engagement), measurement methods using environment-installed sensors and questionnaire surveys have been often used. However, these approaches are not practical in continuous data collection, due to the large burden for people. Recently, in association with advancing sensing technology with IoTs, sensing by small sensors and wearable devices has become possible easily. In this paper, we aim to establish a simple and general sensing method based on a mobile application for measuring physiological and psychological state of office workers and environmental state. Through the experiment for 2-3 weeks involving 60 office workers of four Japanese companies by using our application, we succeeded to create a dataset of physiological, environment, and mental state. This paper explains the developed mobile application, experimental procedure, and a summary of the data collected in the experiment. |
WristSense 2020: 上記データから不安抑うつ傾向と睡眠の関係を調べた研究
Shuichi Fukuda; Yuki Matsuda; Yutaka Arakawa; Keiichi Yasumoto; Yuri Tani Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor Inproceedings WristSense 2020: 6th Workshop on Sensing Systems and Applications using Wrist Worn Smart Devices (WristSense 2020), 2020. @inproceedings{Fuku2003:Predicting, title = {Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor}, author = {Shuichi Fukuda and Yuki Matsuda and Yutaka Arakawa and Keiichi Yasumoto and Yuri Tani}, year = {2020}, date = {2020-03-23}, booktitle = {WristSense 2020: 6th Workshop on Sensing Systems and Applications using Wrist Worn Smart Devices (WristSense 2020)}, abstract = {In recent years, researches on recognizing daily behavior and psychological / physiological states has been actively conducted to change the behavior of workers working in companies. In this paper, we analyzed Occupational Health questionnaire named DAMS on waking-up time and daily sleep data that are acquired from wearable devices in 2--3 weeks experiment of 60 office workers working at five general companies. By using a machine learning method, our binary Balanced Random Forest model predicts depression, positive, and anxiety moods in two levels, high and low. As a result of Leave One Person Out cross validation, it was confirmed that our model estimated with the F1 values of depression mood: 0.776, positive mood: 0.610, anxiety mood: 0.756. Moreover, we evaluated the variance of the three estimations among subjects by the box chart. It was confirmed that there is variance in estimation accuracy for each subject.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In recent years, researches on recognizing daily behavior and psychological / physiological states has been actively conducted to change the behavior of workers working in companies. In this paper, we analyzed Occupational Health questionnaire named DAMS on waking-up time and daily sleep data that are acquired from wearable devices in 2--3 weeks experiment of 60 office workers working at five general companies. By using a machine learning method, our binary Balanced Random Forest model predicts depression, positive, and anxiety moods in two levels, high and low. As a result of Leave One Person Out cross validation, it was confirmed that our model estimated with the F1 values of depression mood: 0.776, positive mood: 0.610, anxiety mood: 0.756. Moreover, we evaluated the variance of the three estimations among subjects by the box chart. It was confirmed that there is variance in estimation accuracy for each subject. |
PerVehicle 2020: 音分析による車両検知手法の改善
Masato Uchino; Billy Dawton; Yuki Hori; Shigemi Ishida; Shigeaki Tagashira; Yutaka Arakawa; Akira Fukuda Initial Design of Two-Stage Acoustic Vehicle Detection System for High Traffic Roads Inproceedings PerVehicle 2020: 2nd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle 2020), 2020. @inproceedings{Uchi2003:Initial, title = {Initial Design of Two-Stage Acoustic Vehicle Detection System for High Traffic Roads}, author = {Masato Uchino and Billy Dawton and Yuki Hori and Shigemi Ishida and Shigeaki Tagashira and Yutaka Arakawa and Akira Fukuda}, year = {2020}, date = {2020-03-23}, booktitle = {PerVehicle 2020: 2nd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle 2020)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |