Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases

BACKGROUND: Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread types of assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers, but it is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable the quantification of three-dimensional movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested. METHODS: To compare video-based analysis with TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings, we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-times sit-to-stand) while capturing additional disease-specific signatures of movement. RESULTS: Video-based biomechanical analysis reproduced all four TFTs (r>0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (P=0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement, such as differences in gait kinematics, that are not evident in TFTs. CONCLUSIONS: Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions. (Funded by the Wu Tsai Human Performance Alliance and others.).