Surface electromyography evaluation for decoding hand motor intent in children with congenital upper limb deficiency

• Published in: Scientific reports 2024-12, Vol.14 (1), p.31741, Article 31741
• Main Authors: Battraw, Marcus A., Fitzgerald, Justin, Winslow, Eden J., James, Michelle A., Bagley, Anita M., Joiner, Wilsaan M., Schofield, Jonathon S.
• Format: Article
• Language: English
• Subjects: 631/378/2632; 692/308/3187; Adolescent; Algorithms; Artificial Limbs; Biological control; Child; Child, Preschool; Children; Classification; Elbow; Electromyography; Electromyography - methods; Female; Hand - physiology; Humanities and Social Sciences; Humans; Male; Motor task performance; Movement - physiology; multidisciplinary; Muscles; Prostheses; Prosthetics; Science; Science (multidisciplinary); Upper Extremity - physiopathology; Upper Extremity Deformities, Congenital - physiopathology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-82519-z

Children born with congenital upper limb absence exhibit consistent and distinguishable levels of biological control over their affected muscles, assessed through surface electromyography (sEMG). This represents a significant advancement in determining how these children might utilize sEMG-controlled dexterous prostheses. Despite this potential, the efficacy of employing conventional sEMG classification techniques for children born with upper limb absence is uncertain, as these techniques have been optimized for adults with acquired amputations. Tuning sEMG classification algorithms for this population is crucial for facilitating the successful translation of dexterous prostheses. To support this effort, we collected sEMG data from a cohort of N = 9 children with unilateral congenital below-elbow deficiency as they attempted 11 hand movements, including rest. Five classification algorithms were used to decode motor intent, tuned with features from the time, frequency, and time–frequency domains. We derived the congenital feature set (CFS) from the participant-specific tuned feature sets, which exhibited generalizability across our cohort. The CFS offline classification accuracy across participants was 73.8% ± 13.8% for the 11 hand movements and increased to 96.5% ± 6.6% when focusing on a reduced set of five movements. These results highlight the potential efficacy of individuals born with upper limb absence to control dexterous prostheses through sEMG interfaces.