Real-time myoelectric control of wrist/hand motion in Duchenne muscular dystrophy: A case study

• Published in: Frontiers in robotics and AI Vol. 10; p. 1100411
• Main Authors: Nizamis, Kostas, Ayvaz, Anıl, Rijken, Noortje H M, Koopman, Bart F J M, Sartori, Massimo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 06.04.2023
• Subjects: admittance; Duchenne muscular dystrophy; forearm; intention decoding; myoelectric control; pattern recognition; Robotics and AI; surface electromyography; Duchenne muscular dystrophy; myoelectric control; forearm; wrist; admittance; intention decoding; pattern recognition
• ISSN: 2296-9144; 2296-9144
• DOI: 10.3389/frobt.2023.1100411

Duchenne muscular dystrophy (DMD) is a genetic disorder that induces progressive muscular degeneration. Currently, the increase in DMD individuals' life expectancy is not being matched by an increase in quality of life. The functioning of the hand and wrist is central for performing daily activities and for providing a higher degree of independence. Active exoskeletons can assist this functioning but require the accurate decoding of the users' motor intention. These methods have, however, never been systematically analyzed in the context of DMD. This case study evaluated direct control (DC) and pattern recognition (PR), combined with an admittance model. This enabled customization of myoelectric controllers to one DMD individual and to a control population of ten healthy participants during a target-reaching task in 1- and 2- degrees of freedom (DOF). We quantified real-time myocontrol performance using target reaching times and compared the differences between the healthy individuals and the DMD individual. Our findings suggest that despite the muscle tissue degeneration, the myocontrol performance of the DMD individual was comparable to that of the healthy individuals in both DOFs and with both control approaches. It was also evident that PR control performed better for the 2-DOF tasks for both DMD and healthy participants, while DC performed better for the 1-DOF tasks. The insights gained from this study can lead to further developments for the intuitive multi-DOF myoelectric control of active hand exoskeletons for individuals with DMD.