Excitation of natural spinal reflex loops in the sensory-motor control of hand prostheses

Sagastegui Alva, Patrick G. ; Boesendorfer, Anna ; Aszmann, Oskar C. ; Ibáñez, Jaime (Universidad de Zaragoza) ; Farina, Dario

Resumen: Sensory feedback for prosthesis control is typically based on encoding sensory information in specific types of sensory stimuli that the users interpret to adjust the control of the prosthesis. However, in physiological conditions, the afferent feedback received from peripheral nerves is not only processed consciously but also modulates spinal reflex loops that contribute to the neural information driving muscles. Spinal pathways are relevant for sensory-motor integration, but they are commonly not leveraged for prosthesis control. We propose an approach to improve sensory-motor integration for prosthesis control based on modulating the excitability of spinal circuits through the vibration of tendons in a closed loop with muscle activity. We measured muscle signals in healthy participants and amputees during different motor tasks, and we closed the loop by applying vibration on tendons connected to the muscles, which modulated the excitability of motor neurons. The control signals to the prosthesis were thus the combination of voluntary control and additional spinal reflex inputs induced by tendon vibration. Results showed that closed-loop tendon vibration was able to modulate the neural drive to the muscles. When closed-loop tendon vibration was used, participants could achieve similar or better control performance in interfaces using muscle activation than without stimulation. Stimulation could even improve prosthetic grasping in amputees. Overall, our results indicate that closed-loop tendon vibration can integrate spinal reflex pathways in the myocontrol system and open the possibility of incorporating natural feedback loops in prosthesis control.

Idioma: Inglés

DOI: 10.1126/scirobotics.adl0085

Año: 2024

Publicado en: Science robotics 9, 90 (2024)
ISSN: 2470-9476

Factor impacto JCR: 27.5 (2024)

Categ. JCR: ROBOTICS rank: 1 / 48 = 0.021 (2024) - Q1 - T1

Factor impacto SCIMAGO: 5.94 - Artificial Intelligence (Q1) - Mechanical Engineering (Q1) - Control and Optimization (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/810346/EU/Calibrating and Improving Mechanistic models of Biodiversity/CLIMB
Financiación: info:eu-repo/grantAgreement/ES/MICINN/CNS2022-135366
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)