Muscle weakness assessment tool for automated therapy selection in elbow rehabilitation

• Published in: Robotica Vol. 41; no. 3; pp. 833 - 849
• Main Authors: Gupta, Sakshi, Agrawal, Anupam, Singla, Ekta
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.03.2023
• Subjects: Advances in Medical Robotics; Algorithms; Curve fitting; Data collection; Data points; Design parameters; Digitization; Elbow; Evaluation; Limbs; Mathematical models; Motor ability; Muscles; Neurological diseases; Patients; Rehabilitation; Robotics; Sensors; Signal processing; Trajectories; upper-limb exoskeleton; task-based device; muscles weakness assessment tool; rehabilitation robotics; electromyography
• ISSN: 0263-5747; 1469-8668
• DOI: 10.1017/S0263574722000844

Clinical observations and subjective judgements have traditionally been used to evaluate patients with muscular and neurological disorders. As a result, identifying and analyzing functional improvements are difficult, especially in the absence of expertise. Quantitative assessment, which serves as the motivation for this study, is an essential prerequisite to forecast the task of the rehabilitation device in order to develop rehabilitation training. This work provides a quantitative assessment tool for muscle weakness in the human upper limbs for robotic-assisted rehabilitation. The goal is to map the assessment metrics to the recommended rehabilitation exercises. Measurable interaction forces and muscle correlation factors are the selected parameters to design a framework for muscular nerve cell condition detection and appropriate limb trajectory selection. In this work, a data collection setup is intended for extracting muscle intervention and assessment using MyoMeter, Goniometer and surface electromyography data for upper limbs. Force signals and human physiological response data are evaluated and categorized to infer the relevant progress. Based upon the most influencing muscles, curve fitting is performed. Trajectory-based data points are collected through a scaled geometric Open-Sim musculoskeletal model that fits the subject’s anthropometric data. These data are found to be most suitable to prescribe relevant exercise and to design customized robotic assistance. Case studies demonstrate the approach’s efficacy, including optimally synthesized automated configuration for the desired trajectory.