IEEE PerCom 2020内ワークショップで４件の発表

@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},
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}
}

@inproceedings{Mats2003:Analysis,
title = {Analysis of Tourists' Nationality Effects on Behavior-based Emotion and Satisfaction Estimation},
author = {Yuki Matsuda and Dmitrii Fedotov and Yutaka Arakawa and Hirohiko Suwa and Wolfgang Minker and Keiichi Yasumoto},
year = {2020},
date = {2020-03-23},
booktitle = {EmotionAware 2020: 4th International Workshop on Emotion Awareness for Pervasive Computing with Mobile and Wearable Devices (EmotionAware 2020)},
address = {Äustin, USA},
abstract = {Smart tourism is attracting attention of researchers in recent years. Its
technologies can be used by tourists in order to obtain useful information
during sightseeing with smart devices etc. To provide suitable and
personalized tourism information according to the situation of tourists,
understanding psychological status during sightseeing, especially,
emotional status and satisfaction level, is important. In this study, we
assume the psychological status of tourists is appearing and represented
through unconscious behaviors during sightseeing such as head/body
movements and facial/vocal expressions, then we are working on emotion and
satisfaction estimation based on sensing and analyzing tourists' behaviors.
Through the evaluation of our estimation model on in-the-wild experiments
with 22 participants including Japanese and Russian on two touristic areas
(Ulm, Germany and Nara, Japan), which reported in our previous paper, it
has been implied the difference in tourists' attributes might give effects
for the estimation. Based on the results, in this paper, we have
statistically analyzed that effects, focusing on tourists' nationality. As
a result of the two-way ANOVA, we found the interaction effect (disordinal
interaction) between tourists' nationality and estimation performance, the
main effect in differences of features, and the main effect in differences
of tourists' nationality. The results imply that we need to take tourists'
nationality into account for building estimation models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@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}
}