AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing
Lauhitya Reddy, Trisha M. Kesar, Hyeokhyen Kwon

TL;DR
AIGaitor is a novel, privacy-preserving motion analysis system that performs end-to-end monocular gait capture and analysis entirely on smartphones, eliminating the need for cloud services.
Contribution
It introduces the first on-device monocular motion capture pipeline with deep learning analysis, optimized for smartphones, enabling accessible clinical gait analysis.
Findings
Processes 10s 4K video in 77s on iPhone 14
Achieves real-time keypoint extraction with lightweight models
Provides sub-millisecond gait classification
Abstract
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…
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