Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control

Abstract

Duchenne muscular dystrophy (DMD) is a genetic disorder that results in progressive muscular degeneration. Although medical advances increased their life expectancy, DMD individuals are still highly dependent on caregivers. Hand/wrist function is central for providing independence, and robotic exoskeletons are good candidates for effectively compensating for deteriorating functionality. Robotic hand exoskeletons require the accurate decoding of motor intention typically via surface electromyography (sEMG). Traditional low-density sEMG was used in the past to explore the muscular activations of individuals with DMD; however, it cannot provide high spatial resolution. This study characterized, for the first time, the forearm high-density (HD) electromyograms of three individuals with DMD while performing seven hand/wrist-related tasks and compared them to eight healthy individuals (all data available online). We looked into the spatial distribution of HD-sEMG patterns by using principal component analysis (PCA) and also assessed the repeatability and the amplitude distributions of muscle activity. Additionally, we used a machine learning approach to assess DMD individuals' potentials for myocontrol. Our analysis showed that although participants with DMD were able to repeat similar HD-sEMG patterns across gestures (similarly to healthy participants), a fewer number of electrodes was activated during their gestures compared to the healthy participants. Additionally, participants with DMD activated their muscles close to maximal contraction level (0.63 ± 0.23), whereas healthy participants had lower normalized activations (0.26 ± 0.2). Lastly, participants with DMD showed on average fewer PCs (3), explaining 90% of the complete gesture space than the healthy (5). However, the ability of the DMD participants to produce repeatable HD-sEMG patterns was unexpectedly comparable to that of healthy participants, and the same holds true for their offline myocontrol performance, disproving our hypothesis and suggesting a clear potential for the myocontrol of wearable exoskeletons. Our findings present evidence for the first

An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion

“…Recently, high-density surface EMG is used to collect the highresolution signals over the forearm. Therefore, the spatial distribution of the muscle activities can be identified and clustered to increase the fidelity of the EMG signals [24]. Furthermore, the passive tendon force is largely different in wrist flexion/extension motion which also results in estimation errors.…”

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human–Robot Collaborative Technologies

“…The challenge is to enable symbiotic physical HRC by incorporating accurate subject-specific computer models of each individual's neuro-musculoskeletal system to enable appropriate anticipation mechanisms. This is crucial to estimate muscle and joint stiffness accurately to determine the onset of excessive rigidity, which may be related to fatigue or negative compensatory strategies ( 95 ).…”

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

“…DMD is an X chromosome-linked progressive neuromuscular disease that results in muscle fiber degeneration, motor deficiencies [ 74 ], and shortened life expectancy [ 75 ]. DMD is the most common form of muscular dystrophy [ 76 ], with an incidence of 1 out of 4000 male births [ 77 ].…”