Instinctive real-time sEMG-based control of prosthetic hand with reduced data acquisition and embedded deep learning training
File(s)
Accepted version
Author(s)
Yang, Zeyu
Clark, Angus
Chappell, Digby
Rojas, Nicolas
Type
Conference Paper
Abstract
Achieving instinctive multi-grasp control of prosthetic hands typically still requires a large number of sensors,
such as electromyography (EMG) electrodes mounted on a
residual limb, that can be costly and time consuming to position,
with their signals difficult to classify. Deep-learning-based EMG
classifiers however have shown promising results over traditional methods, yet due to high computational requirements,
limited work has been done with in-prosthetic training. By
targeting specific muscles non-invasively, separating grasping
action into hold and release states, and implementing data
augmentation, we show in this paper that accurate results for
embedded, instinctive, multi-grasp control can be achieved with
only 2 low-cost sensors, a simple neural network, and minimal
amount of training data. The presented controller, which is
based on only 2 surface EMG (sEMG) channels, is implemented
in an enhanced version of the OLYMPIC prosthetic hand.
Results demonstrate that the controller is capable of identifying
all 7 specified grasps and gestures with 93% accuracy, and is
successful in achieving several real-life tasks in a real world
setting.
such as electromyography (EMG) electrodes mounted on a
residual limb, that can be costly and time consuming to position,
with their signals difficult to classify. Deep-learning-based EMG
classifiers however have shown promising results over traditional methods, yet due to high computational requirements,
limited work has been done with in-prosthetic training. By
targeting specific muscles non-invasively, separating grasping
action into hold and release states, and implementing data
augmentation, we show in this paper that accurate results for
embedded, instinctive, multi-grasp control can be achieved with
only 2 low-cost sensors, a simple neural network, and minimal
amount of training data. The presented controller, which is
based on only 2 surface EMG (sEMG) channels, is implemented
in an enhanced version of the OLYMPIC prosthetic hand.
Results demonstrate that the controller is capable of identifying
all 7 specified grasps and gestures with 93% accuracy, and is
successful in achieving several real-life tasks in a real world
setting.
Date Acceptance
2022-01-31
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Source
IEEE International Conference on Robotics and Automation
Publication Status
Accepted