Assessment of Gesture Accuracy for a Multi-Electrode EMG-Sensor-Array-Based Prosthesis Control System

• Published in: Prosthesis (Basel, Switzerland) Vol. 7; no. 4; p. 99
• Main Authors: Sharma, Vinod, Lloyd, Erik, Faltys, Mike, Ortiz-Catalan, Max, Glass, Connor
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Accuracy; Activities of daily living; Amputation; Arrays; Control algorithms; Electrodes; Electromyography; Informed consent; Muscle contraction; Performance evaluation; Prostheses; Sensors; United States > US
• ISSN: 2673-1592; 2673-1592
• DOI: 10.3390/prosthesis7040099

Background: Upper limb loss significantly impacts quality of life, and whereas myoelectric prostheses restore some function, conventional surface electromyography (sEMG) systems face challenges like poor signal quality, high cognitive burden, and suboptimal control. Phantom X, a novel implantable electrode-array-based system leveraging machine learning (ML), aims to overcome these limitations. This feasibility study assessed Phantom X’s performance using non-invasive surface EMG electrodes to approximate implantable system behavior. Methods: This single-arm, non-randomized study included 11 participants (9 able-bodied, 2 with transradial amputation) fitted with a 32-electrode cutaneous array around the forearm. EMG signals were processed through an ML algorithm to control a desk-mounted prosthesis. Performance was evaluated via gesture accuracy (GA), modified gesture accuracy (MGA), and classifier gesture accuracy (CGA) across 11 hand gestures in three arm postures. User satisfaction was also assessed among the two participants with transradial amputation. Results: Phantom X achieved an average GA of 89.0% ± 6.8%, MGA of 96.8% ± 2.0%, and CGA of 93.6% ± 4.1%. Gesture accuracy was the highest in the Arm Parallel posture and the lowest in the Arm Perpendicular posture. Thumbs Up had the highest accuracy (100%), while Index Point and Index Tap gestures showed lower performance (70% and 79% GA, respectively). The mean latency between EMG onset and gesture detection was 250.5 ± 145.9 ms, with 91% of gestures executed within 500 ms. The amputee participants reported high satisfaction. Conclusions: This study demonstrates Phantom X’s potential to enhance prosthesis control through multi-electrode EMG sensing and ML-based gesture decoding. The non-invasive evaluation suggests high accuracy and responsiveness, warranting further studies with the implantable system to assess long-term usability and real-world performance. Phantom X may offer a superior alternative to conventional sEMG-based control, potentially reducing cognitive burden and improving functional outcomes for upper limb amputees.