@inproceedings{sensys2020_selfguard,
title = {SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic},
author = {Yuuki Nishiyama and Takuro Yonezawa and Kaoru Sezaki},
url = {http://sensys.acm.org/2020/
https://youtu.be/KYmvCHl_U7g},
doi = {10.1145/3384419.3430592},
isbn = {9781450375900},
year = {2020},
date = {2020-11-16},
urldate = {2020-11-16},
booktitle = {Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Virtual Event, Japan},
number = {2},
pages = {780–781},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {SenSys '20},
abstract = {Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.},
key = {self-tracking, mobile sensing, GPS, COVID-19},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}