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