HMDミーティングでFitbitを用いた研究について講演

@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}

}

@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}

}