aiqbench analysis
This research demonstrates a practical application of computer vision and large language models (GPT-4o) for real-time biomechanical analysis in fitness applications, leveraging the Model Context Protocol to connect pose estimation data with structured feedback. By utilizing standard smartphone cameras instead of expensive motion capture suits, the study highlights how accessible AI tools can democratize high-quality form correction and injury risk assessment for High-Intensity Interval Training. The findings confirm that integrating these AI-driven visual analytics into VR environments significantly enhances user immersion and enjoyment compared to traditional non-VR setups.
Smartphone-Aided VR Exergames for HIIT: Gamifying Fitness with Real-Time Pose Estimation
arXiv:2611.06734v1 Announce Type: cross Abstract: This paper introduces an innovative virtual reality (VR) exergame system designed to gamify High-Intensity Interval Training (HIIT) workouts through 11 predefined combo moves, including squat-punch combinations, bilateral arm strikes, and diagonal stretch combos. Unlike conventional tracking methods that often rely on wearable motion capture suits or limited headset-based sensors, our system uses a standard smartphone camera placed on a tripod to capture full-body movement. This approach offers a cost-effective and scalable alternative, particularly well-suited to the fast, dynamic motions typical of HIIT routines. The smartphone footage is processed in real time using a pose estimation algorithm, with extracted biomechanical data analyzed by a backend server built on the Model Context Protocol (MCP), Anthropic's open standard for connecting AI models to structured data sources and tools, and a GPT-4o large language model. The system generates personalized assessments of form accuracy, movement efficiency, exercise intensity, and potential injury risks, tailored specifically to high-impact, time-sensitive exercises. A within-subject study involving 20 participants (ages 18-25) compared experiences in VR and Non-VR HIIT sessions. Results indicated significantly higher enjoyment (Mean = 3.5 vs 2.8, p = 0.02), improved immersion/focus (Mean = 3.2 vs 2.8, p = 0.03), and increased likelihood to recommend (Mean = 3.2 vs 2.7, p = 0.04) under the VR condition. Confidence to continue did not differ significantly between conditions (p = 0.15), while perceived intensity remained comparable. The MCP system showed strong agreement with expert evaluation (87.5%, N = 10 sessions, one certified trainer), supporting its reliability as a feedback tool.
Abstract:This paper introduces an innovative virtual reality (VR) exergame system designed to gamify High-Intensity Interval Training (HIIT) workouts through 11 predefined combo moves, including squat-punch combinations, bilateral arm strikes, and diagonal stretch combos. Unlike conventional tracking methods that often rely on wearable motion capture suits or limited headset-based sensors, our system uses a standard smartphone camera placed on a tripod to capture full-body movement. This approach offers a cost-effective and scalable alternative, particularly well-suited to the fast, dynamic motions typical of HIIT routines. The smartphone footage is processed in real time using a pose estimation algorithm, with extracted biomechanical data analyzed by a backend server built on the Model Context Protocol (MCP), Anthropic's open standard for connecting AI models to structured data sources and tools, and a GPT-4o large language model. The system generates personalized assessments of form accuracy, movement efficiency, exercise intensity, and potential injury risks, tailored specifically to high-impact, time-sensitive exercises. A within-subject study involving 20 participants (ages 18-25) compared experiences in VR and Non-VR HIIT sessions. Results indicated significantly higher enjoyment (Mean = 3.5 vs 2.8, p = 0.02), improved immersion/focus (Mean = 3.2 vs 2.8, p = 0.03), and increased likelihood to recommend (Mean = 3.2 vs 2.7, p = 0.04) under the VR condition. Confidence to continue did not differ significantly between conditions (p = 0.15), while perceived intensity remained comparable. The MCP system showed strong agreement with expert evaluation (87.5%, N = 10 sessions, one certified trainer), supporting its reliability as a feedback tool.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2611.06734 [cs.HC]
(or arXiv:2611.06734v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2611.06734 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 347 words