The following 4 workshop papers will appear in IEEE PerCom 2020 held in Texas, Austin.
Yuri Tani; Shuichi Fukuda; Yuki Matsuda; Sozo Inoue; Yutaka Arakawa
WorkerSense: Mobile Sensing Platform for Collecting Physiological, Mental, and Environmental State of Office Workers Proceedings Article
In: 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.
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.
Shuichi Fukuda; Yuki Matsuda; Yutaka Arakawa; Keiichi Yasumoto; Yuri Tani
Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor Proceedings Article
In: 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},
url = {https://arakawa-lab.com/wp-content/uploads/2022/08/1570616568-stamped-e.pdf
https://ieeexplore.ieee.org/document/9156176},
year = {2020},
date = {2020-03-23},
urldate = {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.
/ 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.
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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 Proceedings Article
In: 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}
}