  Biomechanical biofeedback may enhance rehabilitation and provide clinicians
with more objective task evaluation. These feedbacks often rely on expensive
motion capture systems, 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
low-cost RGBD camera 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 RGBD camera and an optoelectronic camera system,
considered as a reference. Muscle activity and external load were recorded
using eight EMG 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$\pm$3.9 mm. For the
biomechanical analysis, the level of agreement was sensitive to whether the
same marker set was used. For example, joint angle differences averaging
2.3$\pm$2.8{\deg} when using the same marker set, compared to 4.5$\pm$2.9{\deg}
otherwise. Biofeedback from the RGBD camera was provided at 63 Hz. Our study
introduces a novel method for using an RGBD camera as a low-cost motion capture
solution, emphasizing its potential for accurate kinematic reconstruction and
comprehensive upper-limb biomechanical studies.
