arXiv cs.AI· Abhiram Kothagundu, Pheelip Pritipal Raipure, Abhijit Chandrakant Patil, Himangshu Sarma, Christian Eichhorn, David A. Plecher, Johanna Pirker·· 11 小时前AI 评分26
智能手机摄像头驱动的 VR 健身游戏：用实时姿态估计为 HIIT 增加趣味性
Smartphone-Aided VR Exergames for HIIT: Gamifying Fitness with Real-Time Pose Estimation
AI 导读
一项研究提出用三脚架上的普通智能手机摄像头实时捕捉全身动作，驱动 VR 健身游戏完成 11 种预设组合动作（如深蹲出拳、双臂击打、斜向拉伸），替代穿戴式动捕设备。
正文 · 原文
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.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06734 [cs.HC] |
| (or arXiv:2611.06734v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06734 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Himangshu Sarma [view email]
[v1]
Wed, 4 Nov 2026 16:18:35 UTC (4,274 KB)
来源：arXiv cs.AI · arxiv.org