Assessment of a markerless motion analysis system for manual wheelchair application

Abstract

Background: Wheelchair 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.

Results: This 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).

Conclusions: The 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.

Keywords: Manual wheelchair; Markerless motion capture; Musculoskeletal models; Pediatric rehabilitation.

Fig. 1

Conceptual Design and Configuration of the Markerless Wheelchair Analysis System. Subject is stationary on roller system, with a single Kinect sensor positioned in the center, anterior to the subject (for static trial), and two Kinect sensors positioned laterally, to the left and right of the subject (for dynamic trials) – the sensors are moved between trials and a total of two are needed

Fig. 2

Personal Wheelchair Platform. Used to support the wheelchair and provide anthropometrically correct resistance

Fig. 3

Block Diagram of Markerless Kinematic Processing Algorithm. Phases 1, 2, and 3 of processing referenced in text are denoted by boxed regions

Fig. 4

Example Clinical Outputs. Joint Kinematics and Spatiotemporal Parameters for exemplar subject, age 15, with spina bifida – Joint kinematics (top), musculotendon excursion (bottom, left) and propulsion pattern (bottom, right). The subject propelled using the same wheelchair and settings used for everyday mobility, at a self-selected speed and propulsion pattern. The thin lines represent individual trials, and thick lines are average of all trials for left and right extremities

Fig. 5

Inter-Trial Pearson Correlation for Categorical Metrics