SelfGuard: Enhancing Infection-prevention Behaviors by Gamified Feedback

Under the circumstance of the rapid spread of the COVID-19 pandemic, enhancing human’s awareness of self-protection is one practical method to slow down the epidemic. In this study, we utilize mobile sensing to track human activity and guide human’s epidemic prevention behavior by gamified feedback techniques by our developed application SelfGuard. Virtually, human’s self-protection awareness is affected by many factors and the measures to enhance people’s self-protection behavior against the epidemic COVID-19 has always been an unresolved issue. In order to search for factors that influence human’s self-protection behavior, we analyzed the relationships between various human activities and the percentage complete of human’s self-protection behavior and we have extracted some more general conclusions from the results.

Based on our data analysis results, we also made some proposals to enhance self-protection behavior. Meanwhile, our study illustrates the effectiveness of the method that analyzes human self-protection behavior through mobile sensing. Our study also validates the effectiveness of persuasive technology on human’s self-protection behavior against the COVID-19 pandemic and therefore we advocate enhancing human’s self-protection awareness through external intervention and guidance by smart device. In order to analyze quantitatively the relationship between human activities and self-protection awareness and then improve self-protection behavior against COVID-19 more efficiently, we are designing new application and plan to carry out larger scale experiments.

Related papers

Yuuki Nishiyama, Takuro Yonezawa, Kaoru Sezaki: SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic. In: the 18th ACM Conference on Embedded Networked Sensor Systems (SenSys '20), COVID-19 Pandemic Response, Virtual Event, Japan , pp. 780–781, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-7590-0/20/11.

陳美怡, 幡井皓介, 西山勇毅, 瀬崎薫: 感染症予防行動を促進させるインセンティブモデルの構築に向けて. In: 第20回情報科学技術フォーラム(FIT2021), 情報処理学会, オンライン, 2021.

陳美怡, 幡井皓介, 西山勇毅, 瀬崎薫: 感染症予防行動を促進させるインセンティブモデルに関する一検討. In: 研究報告ユビキタスコンピューティングシステム(UBI), pp. 1–7, 情報処理学会, 2021, ISSN: 2188-8698.

Liqiang Xu, Yuuki Nishiyama, Kaoru Sezaki: Enhancing Self-protection: What Influences Human’s Epidemic Prevention Behavior during the COVID-19 Pandemic. In: Distributed, Ambient and Pervasive Interactions, Springer International Publishing, Cham, 2022.

Links

https://sites.google.com/g.ecc.u-tokyo.ac.jp/selfguard/

https://apps.apple.com/jp/app/selfguard/id1507750703

SelfGuard: Enhancing Infection-prevention Behaviors by Gamified Feedback