Smartphone Exergames with Real-Time Markerless Motion Capture: Challenges and Trade-offs

TL;DR

This paper discusses the development of smartphone-based exergames utilizing real-time markerless motion capture, highlighting challenges like balancing accuracy and responsiveness, and proposing future research directions for optimization and user engagement.

Contribution

It introduces the concept of integrating AI-powered markerless motion capture into mobile exergames, addressing key challenges and outlining future research avenues.

Findings

Identified key challenges in accuracy and real-time performance.

Proposed directions for optimizing AI models for mobile use.

Emphasized importance of user-centered design in exergame development.

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.