TEACHING BIOMECHANICAL FEATURES OF HEMIPLEGIC GAIT USING OpenCap.ai: A VALIDATION STUDY FOR CASE- BASED LEARNING
DOI:
https://doi.org/10.17501/24246700.2026.12105Keywords:
biomechanics, education, health care student, AI platformAbstract
Developing competency in gait analysis is essential for healthcare students involved in stroke rehabilitation, particularly for identifying biomechanical deviations associated with hemiplegic gait. Although three-dimensional motion capture (3DMoCap) is considered the reference standard for gait analysis, its high cost, technical complexity, and limited availability restrict its integration into routine educational activities. This case study evaluated the validity of OpenCap.ai, a smartphone-based markerless motion capture system, as an instructional tool for case-based learning in hemiplegic gait assessment. Lower-limb kinematic data were obtained during walking from a hemiplegic patient, focusing on sagittal-plane joint motion at the hip, knee, and ankle. Joint kinematic waveforms derived from OpenCap.ai were compared with those from a laboratory-based BTS 3DMoCap system using Lin’s concordance correlation coefficients (CCC). On the affected limb, OpenCap.ai demonstrated excellent concordance with 3DMoCap for hip (CCC = 0.977) and knee (CCC = 0.982) kinematics, effectively capturing reduced flexion patterns. Agreement at the affected ankle was poor (CCC = 0.420), and sound limb concordance ranged from moderate to poor. These results suggest that while OpenCap.ai does not fully replicate laboratory-grade precision for distal joints, it provides sufficiently accurate representations of proximal gait abnormalities, offering a low-cost platform to support case- based teaching.
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