Myoelectric prosthesis control using recurrent convolutional neural network regression mitigates the limb position effect

Many myoelectric upper limb prosthesis controllers use pattern
recognition, a method that learns and recognizes patterns of
electromyographic (EMG) signals produced by the user’s residual limb
muscles to predict and execute device movements. Such control becomes
unreliable in high limb positions—a problem known as the limb position
effect. Pattern recognition often uses a classification algorithm; simple
to implement, but limits user-initiated control to only one device
movement at a time, at a single speed. To combat position-related control
deficiencies and classification controller constraints, we developed and
tested two recurrent convolutional neural network (RCNN) pattern
recognition-based solutions: (1) an RCNN classification controller that
uses EMG plus positional inertial measurement unit (IMU) signals to offer
one-speed, sequential movement control; and (2) an RCNN regression
controller that uses the same data capture technique to offer simultaneous
control of multiple movements and device movement velocity. We assessed
both RCNN controllers by comparing them to a commonly used linear
discriminant analysis classification controller (LDA-Baseline).
Participants without upper limb impairment were recruited to perform
multipositional tasks while wearing a simulated prosthesis. Both RCNN
classification and regression controllers showed improved functional task
performance over LDA-Baseline, in 11 and 38 out of 115 metrics,
respectively. This work contributes an RCNN regression-based controller
that provides accurate, simultaneous, and proportional movements to
EMG-based technologies including prostheses, exoskeletons, and even
muscle-activated video games.