Assessment of a markerless motion analysis system for manual wheelchair application

Abstract

BackgroundWheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, with the ease of use of a standardized outcome assessment, would be optimal for repeated, longitudinal assessment of manual wheelchair users’ therapeutic progress, but has yet to be offered.ResultsThis work evaluates a markerless motion analysis system for manual wheelchair mobility in clinical, community, and home settings. This system includes Microsoft® Kinect® 2.0 sensors, OpenSim musculoskeletal modeling, and an automated detection, processing, and training interface. The system is designed to be cost-effective, easily used by caregivers, and capable of detecting key kinematic metrics involved in manual wheelchair propulsion. The primary technical advancements in this research are the software components necessary to detect and process the upper extremity kinematics during manual wheelchair propulsion, along with integration of the components into a complete system. The study defines and evaluates an adaptable systems methodology for processing kinematic data using motion capture technology and open-source musculoskeletal models to assess wheelchair propulsion pattern and biomechanics, and characterizes its accuracy, sensitivity and repeatability. Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significantly correlated (p < 0.05).ConclusionsThe system is recommended for use in clinical settings for frequent wheelchair propulsion assessment, provided the limitations in precision are considered. The motion capture-model software bridge methodology could be applied in the future to any motion-capture system or specific application, broadening access to detailed kinematics while reducing assessment time and cost.

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

“…A marker-less system for analysing manual wheelchair propulsion. 66 in comparison with marker-based systems are negligible. However, implementation of existing solutions and testing these solutions for physiotherapy purposes is necessary before any reliable statement on their level of accuracy in comparison to each other is possible.…”

“…Rammer et al 66 propose a system for marker-less motion analysis of manual wheelchair propulsion. Their system requires a minimum of two Microsoft Kinect sensors (hardware devices with camera and microphone) for capturing motion data.…”

Tracking and Characterization of Spinal Cord-Injured Patients by Means of RGB-D Sensors

“…The most diffused solutions in the literature adopt RGB-D sensors that combine a standard RGB video with the depth data of the scene. Rammer et al [17] propose a propulsion platform on which children on a wheelchair perform many pushing cycles. The patient's movements on the wheelchair are acquired by a system composed of three Microsoft Kinect v2 sensors positioned around the wheelchair.…”

The Wheelchair Propulsion Wheel Rotation Angle Function Symmetry in the Propelling Phase: Motion Capture Research and a Mathematical Model

“…This variability of wheelchair acceleration can be observed by measuring the wheelchair's wheel angle of rotation [17]. Assuming that the hand is holding the handrim during the propelling phase, it is possible to analyse the acceleration of the wheelchair in the propelling phase based on the measurement of the hand motion capture [18,19]. Such measurement takes into account the hand slipping in relation to the handrims, which is normally ignored by researchers.…”