Muscle Spindle Model-Based Non-Invasive Electrical Stimulation for Motion Perception Feedback in Prosthetic Hands

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 1316 - 1327
• Main Authors: Ding, Qichuan, Tong, Chenyu, Liu, Dongxu, Yan, Bicen, Wang, Fei, Han, Shuai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025
• Subjects: Adult; Algorithms; Amputation; Amputees - rehabilitation; Artificial Limbs; Biosensors; Electric Stimulation - methods; Electrical stimulation; Electrodes; Encoding; Feedback, Sensory - physiology; Female; Hand - physiology; Hands; Humans; Male; Motion Perception - physiology; motor perception; Movement - physiology; muscle spindle model; Muscle Spindles - physiology; Muscles; non-invasive electrical stimulation; Optical fiber sensors; Propioception; proprioception; Proprioception - physiology; Prosthesis Design; Prosthetic hand; Transcutaneous Electric Nerve Stimulation - methods
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2025.3556726

Prosthetic hands offer significant benefits for patients with hand amputations by partially replicating the function of real hands. However, most current prosthetics lack sensory feedback on movement, leading to a gap in proprioception for users. To bridge this gap and approximate the natural experience of hand use, prosthetic hands must offer detailed motion feedback. This paper introduces a non-invasive electrical stimulation approach, which can provide motion perception feedback through modeling muscle spindles. By employing transcutaneous electrical nerve stimulation (TENS), the method generates artificial sensory signals associated with the movement of a prosthetic hand, potentially restoring a degree of proprioception for patients with hand amputations. We developed an experimental framework involving an electronic prosthetic hand, an electrical stimulator, and surface electrodes to assess our approach. Five able-body and three forearm amputees took part in our experiments. The experimental results indicated that the subjects were able to accurately discern the movement angle of the prosthetic hand, and when the sensory feedback was biomimetic, the subjects were able to identify the prosthetic hand movement state better than using a traditional encoding algorithm that only relied on the current stimulation intensity.