Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 15; p. 4840
• Main Authors: Wang, Bingbin, Li, Jinglin, Hargrove, Levi, Kamavuako, Ernest Nlandu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2024
• Subjects: Adult; Amputation; Amputees - rehabilitation; Artificial Limbs; changes in feature space; Classification; Control systems; Datasets; Discriminant Analysis; electrode shifts; Electrodes; electromyography; Electromyography - methods; Extremities - physiology; Female; Humans; Implants, Artificial; limb positions; Male; myoelectric control; Pattern recognition; Pattern Recognition, Automated - methods; Prostheses; Prosthesis; Young Adult; limb positions; changes in feature space; electrode shifts; electromyography; myoelectric control; pattern recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24154840

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts-two factors that have been suggested to cause classification deterioration-affect classifiers' performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14-21% decrease) with no difference between factors ( > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes' challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.