Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Autor: Guerrero-Mendez CD; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University (UAN), Bogota D.C, Colombia. crguerrero69@uan.edu.co.; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria, 29075-910, Brazil. crguerrero69@uan.edu.co., Lopez-Delis A; Center of Medical Biophysic, University of Oriente, Santiago de Cuba, Cuba., Blanco-Diaz CF; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University (UAN), Bogota D.C, Colombia.; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria, 29075-910, Brazil., Bastos-Filho TF; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria, 29075-910, Brazil., Jaramillo-Isaza S; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University (UAN), Bogota D.C, Colombia., Ruiz-Olaya AF; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University (UAN), Bogota D.C, Colombia.
Jazyk: angličtina
Zdroj: Physical and engineering sciences in medicine [Phys Eng Sci Med] 2024 Jul 02. Date of Electronic Publication: 2024 Jul 02.
DOI: 10.1007/s13246-024-01454-5
Abstrakt: Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1 ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.
(© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)
Databáze: MEDLINE
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