Prosthesis-Guided Training Increases Functional Wear Time And Improves Tolerance To Malfunctioning Inputs Of Pattern Recognition–Controlled Prostheses

Abstract

A remaining barrier to the clinical accessibility of pattern recognition systems is the lack of practical methods to acquire the myoelectric signals required to train the system. Many current methods involve screen-guided training (SGT), where wearers connected to an external computer perform muscle contractions synchronized with a sequence of visual cues. The system complexity prevents easy retraining when signal conditions change. We have developed a method called prosthesis-guided training (PGT), where the prosthesis itself provides the cues by moving through a sequence of preprogrammed motions; screen prompting and external connections are eliminated. Five prosthesis wearers performed a repetitive clothespin placement task using pattern recognition control. Wearers demonstrated similar baseline functionality between systems trained with PGT (10 ± 4 clothespins) and SGT (12 ± 7 clothespins) (p = 0.56). To investigate the efficacy of PGT retraining, real-world issues (e.g. broken wires, external noise) were simulated to accelerate control degradation. Sessions ended when wearers indicated loss of functional control. On average, wearers maintained function through two malfunctioning inputs, placing 48 ± 17 clothespins in 31.6 ± 16.2 minutes when allowed to retrain using PGT. These results suggest that PGT acquires adequate training data and may enable longer-lasting functional use, potentially increasing prosthesis wear time and reducing device rejection.