Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
Artikel i vetenskaplig tidskrift, 2023

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.

transient EMG

pattern recognition

hand wrist prosthetics

cross-subject classifier

Myoelectric control


Daniele D'Accolti

Scuola Superiore Sant'Anna (SSSUP)

Katarina Dejanovic

Scuola Superiore Sant'Anna (SSSUP)

Leonardo Cappello

Scuola Superiore Sant'Anna (SSSUP)

Enzo Mastinu

Scuola Superiore Sant'Anna (SSSUP)

Max Jair Ortiz Catalan

Center for Bionics and Pain Research

Göteborgs universitet

Chalmers, Elektroteknik, System- och reglerteknik

Sahlgrenska universitetssjukhuset

C. Cipriani

Scuola Superiore Sant'Anna (SSSUP)

IEEE Transactions on Neural Systems and Rehabilitation Engineering

1534-4320 (ISSN) 1558-0210 (eISSN)

Vol. 31 208-217


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