Study of the relevance of gender in the classification of hand gestures by electromyography-based recognition systems

• Published in: Research on biomedical engineering Vol. 37; no. 2; pp. 361 - 373
• Main Authors: Freitas, Melissa La Banca, Junior, Jose Jair Alves Mendes, La Banca, Wesley Freitas, Stevan, Sergio Luiz
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original Article; Biomedical equipment; Electromyography; Pattern recognition; Gesture recognition; Biomedical signal processing
• ISSN: 2446-4732; 2446-4740
• DOI: 10.1007/s42600-021-00145-4

Purpose Some factors such as gender, age, physical fitness, and manual dominance are relevant and can influence the recognition of movement patterns using electromyography (EMG). In this scenario, we present an EMG signal analysis for men and women to observe if there is any significant difference. Methods Data from 10 men and 10 women were acquired during the execution of six hand gestures (wrist flexion, wrist extension, wrist flexion to left, wrist extension to right, supination, and pronation) using eight channels armband. Four EMG time-domain signal features were extracted and hand gestures were classified using linear and quadratic discriminant analysis (LDA, QDA), and k-nearest neighbors (KNN) algorithms. Results Data of the feature difference absolute standard deviation value (DASDV) and waveform length (WL) were analyzed based on polar and bar graphs. KNN with 1 nearest neighbor obtained the best results between the classifiers for both men and women. For statistical analyses, the Wilcoxon-Mann-Whitney and Tukey post hoc in Friedman tests were used. The results show that there is no significant difference between data from different genders. Conclusion After analyzing the results obtained and extensive comparison with related works, it was concluded that, for the conditions where the electrodes are positioned equidistantly, evaluating all the muscular groups of a limb (armband format), there was no significant difference observed between the data from different genders. In addition, this allows us to conclude that EMG armband on the forearm can be a good option for robotic systems control without the need for prior gender adjustment.