Hong Yoon Kim
ABSTRACT
COVID-19 has accelerated mobile body-weight fitness services, enabling users to work out at home. Many body-weight exercise recommendation services, claiming to provide advanced personalization using AI technology, have been released. Given that most of these fitness services are self-paced, users’ satisfaction with personalized exercises would greatly influence their commitment and engagement with the service. However, users’ expectations of AI-generated workouts consisting of various body-weight movements have yet to be explored within the HCI community. In this paper, we conducted a Wizard-of-Oz experiment for two weeks with 12 participants to investigate users’ expectations of AI-generated body-weight exercises. Our results show that users expect the personalized system to match their preferred intensity and movement diversity while filtering out movements beyond their capability. We also propose design implications based on the findings.
- Manolis Adamakis. 2021. Physical activity, sleep and weight management in the covid-19 era: A case report. Journal of physical education and sport 21 (2021), 60–65.Google Scholar
- Achraf Ammar, Khaled Trabelsi, Michael Brach, Hamdi Chtourou, Omar Boukhris, Liwa Masmoudi, Bassem Bouaziz, Ellen Bentlage, Daniella How, Mona Ahmed, 2021. Effects of home confinement on mental health and lifestyle behaviours during the COVID-19 outbreak: Insight from the ECLB-COVID19 multicenter study. Biology of sport 38, 1 (2021), 9–21.Google Scholar
- Soumya C Barathi, Daniel J Finnegan, Matthew Farrow, Alexander Whaley, Pippa Heath, Jude Buckley, Peter W Dowrick, Burkhard C Wünsche, James LJ Bilzon, Eamonn O’Neill, 2018. Interactive feedforward for improving performance and maintaining intrinsic motivation in VR exergaming. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 408.Google ScholarDigital Library
- Valerio Bonavolontà, Stefania Cataldi, Adalisa Coluccia, Antonio Giunto, and Francesco Fischetti. 2020. Sustainable Intervention for Health Promotion and Postural Control Improvement: Effects of Home-Based Oculomotor Training. Sustainability (2020).Google Scholar
- Qiuying Chen and SangJoon Lee. 2021. A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform. Applied Sciences 11, 21 (2021), 9927.Google ScholarCross Ref
- Dong-Cheol Chi and Sang-Ho Kim. 2021. Home training trend analysis using newspaper big data and keyword analysis. Journal of the Korea Convergence Society 12, 6 (2021), 233–239.Google Scholar
- Sang D Choi, Liangjie Guo, Donghun Kang, and Shuping Xiong. 2017. Exergame technology and interactive interventions for elderly fall prevention: A systematic literature review. Applied ergonomics 65 (2017), 570–581.Google Scholar
- Babak A. Farshchian and Thomas Vilarinho. 2017. Which Mobile Health Toolkit Should a Service Provider Choose? A Comparative Evaluation of Apple HealthKit, Google Fit, and Samsung Digital Health Platform. In European Conference on Ambient Intelligence.Google Scholar
- Andrew Garbett, Ziedune Degutyte, James Hodge, and Arlene Astell. 2021. Towards understanding people’s experiences of ai computer vision fitness instructor apps. In Designing interactive systems conference 2021. 1619–1637.Google Scholar
- Martin J Gibala, Jonathan P Little, Maureen J MacDonald, and John A Hawley. 2012. Physiological adaptations to low-volume, high-intensity interval training in health and disease. The Journal of physiology 590, 5 (2012), 1077–1084.Google ScholarCross Ref
- Joshua C Haller, Young H Jang, Jack Haller, Lindsay Shaw, and Burkhard C Wünsche. 2019. HIIT The Road: Using Virtual Spectator Feedback in HIIT-based Exergaming. In Proceedings of the Australasian Computer Science Week Multiconference. ACM, 47.Google ScholarDigital Library
- Maged N Kamel Boulos. 2012. Xbox 360 Kinect exergames for health. Games for Health: Research, Development, and Clinical Applications 1, 5 (2012), 326–330.Google ScholarCross Ref
- Peterson MD, Pistilli E, Haff GG, Hoffman EP, and Gordon PM. 2011. Progression of volume load and muscular adaptation during resistance exercise.Eur J Appl Physiol (2011).Google Scholar
- Michael Mutz, Johannes Müller, and Anne K Reimers. 2021. Use of digital media for home-based sports activities during the COVID-19 pandemic: results from the German SPOVID survey. International Journal of Environmental Research and Public Health 18, 9 (2021), 4409.Google ScholarCross Ref
- Seokwoo Song and Doil Kwon. 2022. Bodyweight Exercise based Exergame to Induce High Intensity Interval Training. In Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–4.Google ScholarDigital Library
- Thomas Weber and Heinrich Hussmann. 2022. Tooling for Developing Data-Driven Applications: Overview and Outlook. Proceedings of Mensch und Computer 2022 (2022).Google ScholarDigital Library
- Alfian Noha Zulkarnain, Agus Kristiyanto, and Noer Rachma. 2021. Effectiveness body weight strength training and plyometric in the speed and agility taekwondo athletes. Jurnal SPORTIF : Jurnal Penelitian Pembelajaran (2021).Google Scholar
Index Terms
- Understanding Satisfaction Factors of Personalized Body-weight Exercises
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