{"id":809,"date":"2020-10-26T17:41:52","date_gmt":"2020-10-27T02:41:52","guid":{"rendered":"https:\/\/www.mcl.iis.u-tokyo.ac.jp\/?p=809"},"modified":"2020-10-26T17:46:05","modified_gmt":"2020-10-27T02:46:05","slug":"acm-sensys2020%e3%81%aecovid-19-pandemic-response%e3%81%ab%e3%80%8cselfguard-semi-automated-activity-tracking-for-enhancing-self-protection-against-the-covid-19-pandemic%e3%80%8d%e3%81%8c%e5%86%8d","status":"publish","type":"post","link":"https:\/\/www.mcl.iis.u-tokyo.ac.jp\/en\/2020\/10\/26\/acm-sensys2020%e3%81%aecovid-19-pandemic-response%e3%81%ab%e3%80%8cselfguard-semi-automated-activity-tracking-for-enhancing-self-protection-against-the-covid-19-pandemic%e3%80%8d%e3%81%8c%e5%86%8d\/","title":{"rendered":"Our paper “SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic” is accepted by ACM SenSys2020 (COVID-19 Pandemic Response session)"},"content":{"rendered":"\n

Our paper “SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic” is accepted by ACM SenSys2020<\/a> (COVID-19 Pandemic Response session). Yuuki Nishiyama (Assistant Professor), Takuro Yonezawa (Associate Professor from Nagoya University), and Kaoru Sezaki (Professor) wrote the paper.<\/p>\n\n\n

Yuuki Nishiyama, Takuro Yonezawa, Kaoru Sezaki: <\/span> SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic<\/span>. In: <\/span>the 18th ACM Conference on Embedded Networked Sensor Systems (SenSys '20), COVID-19 Pandemic Response, Virtual Event, Japan , <\/span>pp. 780\u2013781, <\/span>Association for Computing Machinery, <\/span>New York, NY, USA, <\/span>2020<\/span>, ISBN: 978-1-4503-7590-0\/20\/11<\/span>.<\/span><\/div>\n\n\n\n

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Abstract<\/h2>

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.<\/p>\n\n\n\n

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