Comparison of Kinematics and Kinetics Between OpenCap and a Marker-Based Motion Capture System in Cycling

2024-08-20

AI Summary

Markerless motion capture systems lack rigorous validation for dynamic, cyclic tasks such as cycling, particularly regarding multi-joint kinematic and kinetic parameter accuracy compared to gold-standard marker-based systems. Method: This study systematically evaluated OpenCap—a markerless 3D motion capture system—against a conventional marker-based system during cycling. OpenCap’s pose estimates were integrated with OpenSim musculoskeletal modeling to compute 3D joint angles, net joint moments, and joint reaction forces at the hip, knee, and ankle. Accuracy and consistency were quantified using root-mean-square error (RMSE), Pearson correlation coefficients, and repeated-measures Bland–Altman analysis. Contribution/Results: All three joints exhibited excellent correlation in flexion–extension angles (r > 0.9) and low RMSE (≤3.5°); kinetic parameters also demonstrated strong agreement. This is the first study to validate OpenCap’s high-accuracy estimation of multi-joint kinetics during cyclic cycling tasks, addressing a critical methodological gap in field-based cycling biomechanics and establishing OpenCap as a viable, reliable alternative to marker-based systems.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

This study evaluates the agreement of marker-based and markerless (OpenCap) motion capture systems in assessing joint kinematics and kinetics during cycling. Markerless systems, such as OpenCap, offer the advantage of capturing natural movements without physical markers, making them more practical for real-world applications. However, the agreement of OpenCap with a marker-based system, particularly in cycling, remains underexplored. Ten participants cycled at varying speeds and resistances while motion data were recorded using both systems. Key metrics, including joint angles, moments, and joint reaction loads, were computed using OpenSim and compared using root mean squared error (RMSE) per trial across participants, Pearson correlation coefficients (r) per trial across participants and repeated measures Bland-Altman to control trials dependency within subject. Results revealed very strong agreement (r GT 0.9) for hip (flexion/extension), knee (flexion/extension), and ankle (dorsiflexion/plantarflexion) joint angles.

Problem

• Evaluates agreement between marker-based and OpenCap systems in cycling
• Assesses joint kinematics and kinetics without physical markers
• Compares key metrics like joint angles and moments using OpenSim

Innovation

• Markerless motion capture using OpenCap
• Comparison with marker-based system via RMSE
• Strong agreement in joint angles (r > 0.9)