{"ID":2896490,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.06669","arxiv_id":"2507.06669","title":"Smartphone Exergames with Real-Time Markerless Motion Capture: Challenges and Trade-offs","abstract":"Markerless Motion Capture (MoCap) using smartphone cameras is a promising approach to making exergames more accessible and cost-effective for health and rehabilitation. Unlike traditional systems requiring specialized hardware, recent advancements in AI-powered pose estimation enable movement tracking using only a mobile device. For an upcoming study, a mobile application with real-time exergames including markerless motion capture is being developed. However, implementing such technology introduces key challenges, including balancing accuracy and real-time responsiveness, ensuring proper user interaction. Future research should explore optimizing AI models for realtime performance, integrating adaptive gamification, and refining user-centered design principles. By overcoming these challenges, smartphone-based exergames could become powerful tools for engaging users in physical activity and rehabilitation, extending their benefits to a broader audience.","short_abstract":"Markerless Motion Capture (MoCap) using smartphone cameras is a promising approach to making exergames more accessible and cost-effective for health and rehabilitation. Unlike traditional systems requiring specialized hardware, recent advancements in AI-powered pose estimation enable movement tracking using only a mobi...","url_abs":"https://arxiv.org/abs/2507.06669","url_pdf":"https://arxiv.org/pdf/2507.06669v1","authors":"[\"Mathieu Phosanarack\",\"Laura Wallard\",\"Sophie Lepreux\",\"Christophe Kolski\",\"Eugénie Avril\"]","published":"2025-07-09T08:58:59Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}