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Prosthetic rehabilitation of the upper limb through powered prosthetic devices is an active research
area for the past several decades. Multi-degree of freedom prosthetic hands controlled by surface
electromyography signals (sEMG) of the forearm muscles have been developed to restore the lost
limb functionality. A major objective of the current research is the development of intuitive
prosthetic controllers for modern dexterous prosthetic hands. Current state of the art prosthetic
controllers utilize pattern recognition (PR) of multi-channel sEMG signals for decoding the
intended motion class. Despite decades of academic research, a clinically viable PR based
prosthetic controller is yet to be realized.
The PR based classifiers have resulted in more than 90% classification accuracy when evaluated
under controlled laboratory conditions. However, the excellent laboratory performance of these
classifiers is yet to result in their clinical acceptability and commercial availability. As a result, the
currently available prosthetic devices are still based on non-intuitive binary or sequential digital
control. There are several reasons for this academia-industry disparity. The unintended variations
of sEMG signal characteristic due to several confounding factors, including arm position,
adversely affects the performance of pre-trained PR based prosthetic controllers. Researchers have
proposed fusion of auxiliary sensory information for classifier robustness against forearm
positional variations. Sensor fusion techniques including accelerometer-mechanomyography
(ACC-MMG), force-myography (FMG) and magnetic markers (MMs), have been reported with
significant performance improvements.
This study focused on the impact of arm position variations on the performance of PR-based
forearm motion class decoders. A wearable data acquisition system was designed to acquire multichannel sEMG and measure arm position using inertial measurement units. The performance of
support vector machine (SVM) and linear discriminant analysis (LDA) classifiers was evaluated
for training at static positions and for dynamic arm movements to characterize the adverse effects
of arm position variation. Sensor fusion of sEMG and arm position data was evaluated to mitigate
the arm position effect. A comparison of static multi-position training and dynamic arm movement
training was carried out to suggest more pragmatic strategies for classifier training. The sensitivity
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of classifiers to motion class taxonomy was also evaluated. Sixteen motion classes categorized in
distant-taxonomy and close-taxonomy groups were classified using LDA, Linear SVM (LSVM),
Non-Linear SVM (NLSVM) and Multi-layer perception (MLP) classifiers. The results of the study
have shown a significant dependence of LDA and LSVM classifiers and an insignificant
dependence of the NLSVM and MLP classifiers on motion class taxonomy. The results suggest a
more pragmatic selection of motion classes for realistic classifier performance evaluation