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The dexterity of conventional myoelectric prostheses is limited in part bythe small datasets used to train the control algorithms. Variations in surfaceelectrode positioning make it difficult to collect consistent data and toestimate motor intent reliably over time. To address these challenges, wedeveloped an inexpensive, easy-to-don sleeve that can record robust andrepeatable surface electromyography from 32 embedded monopolar electrodes.Embedded grommets are used to consistently align the sleeve with natural skinmarkings (e.g., moles, freckles, scars). The sleeve can be manufactured in afew hours for less than $60. Data from seven intact participants show thesleeve provides a signal-to-noise ratio of 14, a don-time under 11 seconds, andsub-centimeter precision for electrode placement. Furthermore, in a case studywith one intact participant, we use the sleeve to demonstrate that neuralnetworks can provide simultaneous and proportional control of six degrees offreedom, even 263 days after initial algorithm training. We also highlight thatconsistent recordings, accumulated over time to establish a large dataset,significantly improve dexterity. These results suggest that deep learning witha 74-layer neural network can substantially improve the dexterity and stabilityof myoelectric prosthetic control, and that deep-learning techniques can bereadily instantiated and further validated through inexpensive sleeves/socketswith consistent recording locations.

Inexpensive surface electromyography sleeve with consistent electrode  placement enables dexterous and stable prosthetic control through deep  learning