Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera

TL;DR

This study introduces a novel method for using a depth-sensing camera as a low-cost motion capture solution for upper-limb kinematics and suggests its potential for accurate kinematic reconstruction and comprehensive upper-limb biomechanical studies.

Abstract

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$2.8° when using the same marker set, compared to 4.5$\pm$2.9° 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.

Figures (9)

• Figure 1: Participant outfitted with white markers, reflective markers (center of the square), and EMG sensors on the left arm. Manual calibration of the acromion cluster ceglia2024sofamea is shown on the right. The scales on the device facilitate the extraction of anatomical bony landmarks from the 3D cluster's marker positions via an included Python code.
• Figure 2: Experimental setup showing the RGBD camera placement and some Vicon cameras while a participant performs hand cycling.
• Figure 3: Software architecture of the markers tracking from the RGBD camera.
• Figure 4: Graphical user interface for a) blob detection for the arm area and b) manual markers labeling.
• Figure 5: Mean of the 120 cycles of 3D marker trajectories for one participant for minimal-Vicon (red) and RGBD-based (blue) mocap methods.