Biomechanics studies using traditional optical motion capture have been limited by small, homogeneous sample sizes and a focus on single movements, restricting the ability to capture clinically relevant adaptations across daily tasks. These limitations are particularly consequential in heterogeneous musculoskeletal conditions such as knee osteoarthritis (OA), where variability in demographic and clinical characteristics necessitates large, representative samples to identify patient-specific biomechanical intervention targets. Markerless motion capture enables faster, high-throughput data collection and offers the potential for community-based assessments; however, its feasibility of use in clinical populations across diverse tasks remains unclear. This study evaluated the feasibility of community-based, high-throughput markerless biomechanics data collection in individuals with knee OA. Participants (n = 85) completed a series of activities of daily living using a portable markerless motion capture system deployed across two community-based and two on-campus sites. Feasibility was assessed using timing metrics related to research operations (transit, setup, calibration, breakdown), participant workflow (consent, questionnaires, motion capture), and task-specific durations. No significant differences in timing metrics were observed across sites despite logistical and operational challenges. These findings support the feasibility of using high-throughput, community-based markerless motion capture and suggest a viable pathway for addressing long-standing limitations in sample size and representativeness through scalable data collection workflows in biomechanics studies.

The authors have declared no competing interest.

This research was supported by a grant from the National Institute on Aging, Claude D. Pepper Older Americans Independence Center at the University of Florida (P30 AG028740). This project utilized REDCap, which is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR001427). KEC was supported by an Investigator Award from the Rheumatology Research Foundation. RCM was supported by a Herbert Wertheim College of Engineering Deans Research Award from the University of Florida.

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