Yuuki Nishiyama
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.
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- E. K. Choe et al. 2017. Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications. IEEE Pervasive Computing 16, 1 (2017), 74--84.Google ScholarDigital Library
- Jinfeng Li and Xinyi Guo. 2020. COVID-19 Contact-tracing Apps: a Surveyon the Global Deployment and Challenges. arXiv:2005.03599 [cs.CR]Google Scholar
- Yuuki Nishiyama et al. 2020. IOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform. In Distributed, Ambient and Pervasive Interactions. 223--243.Google Scholar
- World Health Organization 2020. COVID-19. https://www.who.int/emergencies/diseases/novel-coronavirus-2019Google Scholar
Index Terms
SelfGuard: semi-automated activity tracking for enhancing self-protection against the COVID-19 pandemic: poster abstract
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