Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera - An exploratory hand-cycling study.

Biomechanical biofeedback may enhance rehabilitation and provide clinicians with more objective task evaluation. These feedbacks often rely on expensive motion capture systems (∼$100000), which restricts their widespread use, leading to the development of computer vision-based methods. These methods are subject to large joint angle errors, considering the upper limb, and exclude the scapula and clavicle motion in the analysis. Our open-source approach offers a user-friendly solution for high-fidelity upper-limb kinematics using a single consumer-grade depth-sensing camera (∼$500) and includes semi-automatic skin marker labeling. Real-time biomechanical analysis, ranging from kinematics to muscle force estimation, was conducted on eight participants performing a hand-cycling motion to demonstrate the applicability of our approach on the upper limb. Markers were recorded by the depth-sensing camera and an optoelectronic camera system, considered as a reference. Muscle activity and external load were recorded using eight electromyography sensors and instrumented hand pedals, respectively. Bland-Altman analysis revealed significant agreements in the 3D markers' positions between the two motion capture methods, with errors averaging 3.3 ± 3.9 mm. The error propagation was low for the biomechanical analysis, with joint angle differences, for example, below 5° when comparing both systems. Biofeedback from the depth-sensing camera was provided at 68 Hz. Our study introduces a novel method for using a depth-sensing camera as a low-cost motion capture solution. Results from healthy participants suggest its potential for accurate kinematic reconstruction and comprehensive upper-limb biomechanical studies. Further investigation is needed to explore its clinical applications in pathological populations.