用手机实现无需云端的隐私保护步态分析，让普通人也能低成本使用专业级运动检测。
AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing
- 将全身动作捕捉与深度学习分析全放在手机本地运行，无需上传数据或依赖云端。
- 10秒4K视频在iPhone 14上77秒完成分析，性能媲美高端云服务器加传输时间。
- 适合康复医疗、健身追踪等场景，特别适合关注隐私和成本的用户。
动作捕捉是人体运动测量的金标准，但受限于成本高、技术复杂和隐私问题，临床应用仍不普及。AIGaitor是一个无需云端、保护隐私的运动分析系统，可在消费级智能手机上通过设备端神经加速器运行无标记单目动作捕捉流程及下游深度学习分析。为指导设计，我们调研了74名康复科医生：92%表示愿意采用准确、经济、易用的AI步态分析工具，其中79.7%认为运营成本高，68.9%缺乏培训，64.9%担忧隐私。随后，我们优化并基准测试了当前单目流程组件的移动端iOS实现，包括2D/3D姿态估计、姿态优化、基于骨架的深度学习分析及视觉-语言模型。一个时间优先的端到端流程可在iPhone 14上于77秒内处理一段10秒的4K 60fps视频，当包含网络传输时，其性能与高端NVIDIA H200云服务器相当：全球移动平均上传速度下为94秒，发达国家Wi-Fi环境下为66秒。轻量级模型如ViTPose-s可实现实时关键点提取，基于骨架的动作识别模型在同一片段上实现亚毫秒级步态分类。据我们所知，AIGaitor是首个展示端到端本地化单目动作捕捉与下游深度学习分析的系统，支持低成本、私密且面向大众的临床可用运动分析。
原文摘要 · Abstract (English)
Motion capture is the gold standard for measuring human movement, but clinical use remains limited by cost, technical complexity, and privacy concerns. AIGaitor is a privacy-preserving, cloud-free motion analysis system that runs markerless monocular motion-capture pipelines and downstream deep-learning analysis entirely on a consumer smartphone using on-device neural accelerators. To motivate its design, we surveyed 74 rehabilitation clinicians: 92 percent said they would adopt an accurate, cost-effective, easy-to-use AI gait analysis tool, while 79.7 percent cited operating cost, 68.9 percent insufficient training, and 64.9 percent privacy concerns as leading barriers. We then optimized and benchmarked mobile iOS implementations of current monocular pipeline components, including 2D and 3D pose estimation, pose optimization, skeleton-based deep-learning analysis, and a vision-language model. A Time-Priority end-to-end on-device pipeline processes a 10 s 4K 60 fps video clip in 77 s on an iPhone 14, matching or beating the same pipeline on a high-end NVIDIA H200 cloud server when network transfer is included: 94 s at global mobile-average uplink and 66 s at developed-world Wi-Fi. Lightweight models such as ViTPose-s achieve real-time keypoint extraction, and skeleton-based action-recognition models provide sub-millisecond gait classification on the same clip. To our knowledge, AIGaitor is the first monocular system to demonstrate end-to-end on-device motion capture and downstream deep-learning analysis, supporting clinically applicable movement analysis that is low-cost, private, and accessible to smartphone users.
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