CAN THE OPENCAP MARKERLESS MOTION CAPTURE FRAMEWORK DETECT LOWER EXTREMITY KINEMATIC CHANGES DURING A FATIGUING RUN?

During endurance running, fatigue-induced biomechanical changes in running form can be detrimental to performance and can increase the risk of injury. A traditional “gold standard” method to study these changes, 3D passive reflective marker-based motion capture systems, presents several barriers to use including financial cost, space, and accessibility limitations. The OpenCap markerless motion capture framework utilizes an open-source algorithm to compute and process kinematics and dynamics of human movement. With 2 or more smartphones and an internet connection, OpenCap provides a more accessible alternative method for motion capture. This validation study investigated the OpenCap framework’s ability to detect kinematic changes at the hip, knee, and ankle due to a fatiguing run when compared to a marker-based motion capture system. It was hypothesized 1) that the lower extremity kinematics measured by OpenCap would significantly differ from those measured with a marker-based motion capture system, and 2) that OpenCap would not be able to detect lower extremity joint angle changes caused by fatigue. Twenty-two participants completed a fatiguing treadmill run, beginning at a selfselected “easy” effort (average RPE ≤ 8) before speeding up in 2-minute intervals until reaching a “somewhat hard” effort (RPE ≥ 13). Participants maintained pace until reaching a “very hard” effort (RPE ≥ 17 or predicted HR ≥ 90% HRMAX). At 2-minute intervals, OpenCap recorded data (120 Hz) from 2 smartphones while a marker-based motion capture system (Vicon) was used to record data (120 Hz) from 9 cameras tracking marker clusters. For each participant, 5 synchronized strides of joint angle data from each leg were time normalized across 100 time points. Kinematics from both systems were used to compute continuous joint angle change scores (Δθ = θPOST - θPRE) and discrete joint angle range of motion (ROMSTRIDE = θMAX - θMIN). A repeated measures ANOVA was used with statistical parameter mapping (SPM) to analyze time-series data and determine if there were significant differences between change scores (Δθ) captured by OpenCap and Vicon. SPM showed that for much of stride, systems detected similar changes. ROMSTRIDE were averaged across 5 synchronized strides from participants’ first interval after jumping (pre-fatigue), and final interval (post-fatigue). ROMSTRIDE detected by each system over time were analyzed using a 2x2 RM-ANOVA at each joint, indicating OpenCap displayed different joint angles than Vicon, except for knee flexion-extension and ankle inversion-eversion. There was a main effect of systems at hip flexion-extension and time-by-system interaction effects at hip abduction-adduction, hip internal rotation-external rotation, and ankle dorsiflexionplantarflexion. However, these interactions showed significant differences between systems pre- and post-fatigue. OpenCap was able to detect kinematic changes due to fatigue. There were main effects of time at knee flexion-extension and ankle inversion-eversion, and interaction effects showed significant differences between OpenCap before and after fatigue. Our findings showed OpenCap’s potential as a tool for detecting visual indicators of fatigue, but caution that joint angles should be validated before making direct comparisons.<p></p>