Selection of muscle and nerve-cuff electrodes for neuroprostheses usingcustomizable musculoskeletal model Dimitra Blana, PhD; Juan G. Hincapie, PhD; Edward K. Chadwick, PhD; Robert F. Kirsch, PhD
Aim • Present systematic approach to muscle and nerve-cuff electrode selection for neuroprosthetic system that considers functional goals, hardware limitations, muscle and nerve anatomy, surgical feasibility. • Relevance • Identifying optimal electrode set for neuroprosthesis is complicated because it depends on characteristics of individual, force capacities of muscles, movements the system aims to restore, and hardware limitations.
Method • Developed electrode-selection method that used customized musculoskeletal model. • Created candidate electrode sets based on desired functional outcomes and hardware limitations of proposed system. • Performed inverse-dynamic simulations to determine proportion of target movements that could be accomplished with each set. • Chose set allowing most movements to be performed as optimal set.
Model Inputs are 11 angles of shoulder and elbow (3 each at sternoclavi-cular, acromioclavicular, and glenohumeraljoints; elbow flexion-extension; and forearm pronation-supination).
Results • Optimal muscle set: • Prime shoulder movers • Deltoid, pectoralis major. • Shoulder stabilizers • Infraspinatus, supraspinatus, subscapularis, serratus anterior, rhomboids. • Elbow flexion-extension • Biceps, brachialis, medial/ lateral triceps. • Forearm pronation/ supination • Pronator quadratus, supinator. • This muscle set had relatively high success rate for simple movements of elbow and forearm and reaching to low-level target such as tabletop.
Conclusion • Nerve-cuff placement and selectivity were important factors in: • Determination of predicted function. • Choice of nerves and muscles to target. • Musculoskeletal models can facilitate development of neuroprosthetic systems by: • Quantifying importance of various muscles on different movements. • Allowing appropriate allocation of stimulating electrodes without time-consuming trial-and-error.