Hierarchical Classification of EMG Signal for Hand and Wrist Gestures and Forces in Myoelectric Control

The loss of an upper limb significantly affects daily activities, making advanced prosthesis control crucial for improving the quality of life. Pattern recognition applied to electromyographic signals has emerged as a leading solution for controlling prosthetic hands; yet, most studies focus solely on steady-state muscle activity, neglecting the transient phase of contraction, thereby limiting real-world applicability. To address this limitation, this study introduces a hierarchical approach that combines an Onset Detection Algorithm, a 9-class steady-state gesture classifier, and a three-level force classifier. Additionally, it investigates Self-selected contraction levels across three grasp types, corresponding to subjectively perceived low, medium, and high forces, chosen according to each participant's preference or perceived exertion. Results demonstrate improved classification accuracy and responsiveness, particularly during early muscle contraction, outperforming state-of-the-art methods. Moreover, optimal contraction levels were found to be grasp-dependent and significantly lower than those commonly used in the literature, emphasizing the need to adjust reference values to reduce fatigue and enhance comfort.