Abstract
The performance of intelligent electromyogram (EMG)-driven prostheses, functioning as artificial alternatives to missing limbs, is influenced by several dynamic factors including: electrode position shift, varying muscle contraction level, forearm orientation, and limb position. The impact of these factors on EMG pattern recognition has been previously studied in isolation, with the combined effect of these factors being understudied. However, it is likely that a combination of these factors influences the accuracy. We investigated the combined effect of two dynamic factors, namely, forearm orientation and muscle contraction levels, on the generalizability of the EMG pattern recognition. A number of recent time- and frequency-domain EMG features were utilized to study the EMG classification accuracy. Twelve intact-limbed and one bilateral transradial (below-elbow) amputee subject were recruited. They performed six classes of wrist and hand movements at three muscular contraction levels with three forearm orientations (nine conditions). Results indicate that a classifier trained by features that quantify the angle, rather than amplitude, of the muscle activation patterns perform better than other feature sets across different contraction levels and forearm orientations. In addition, a classifier trained with the EMG signals collected at multiple forearm orientations with medium muscular contractions can generalize well and achieve classification accuracies of up to 91%. Furthermore, inclusion of an accelerometer to monitor wrist movement further improved the EMG classification accuracy. The results indicate that the proposed methodology has the potential to improve robustness of myoelectric pattern recognition.
Highlights
An artificial hand is an example of a technology that can be used to help a person, with a congenital condition or after an injury that results in the loss of the limb, perform essential activities of daily living (Nazarpour, Cipriani, Farina, & Kuiken, 2014)
We investigated the combined effect of forearm orientation and muscle contraction level on the generalizability of the EMG pattern recognition
With using a subset of well-known EMG feature extraction, we studeid the effects of combined effect of forearm orientation and muscular contraction level on the performance of EMG pattern recognition
Summary
An artificial hand is an example of a technology that can be used to help a person, with a congenital condition or after an injury that results in the loss of the limb, perform essential activities of daily living (Nazarpour, Cipriani, Farina, & Kuiken, 2014). Commercial prosthetic hands are highly sophisticated, offering individual finger movement These prosthetic hands are controlled with an intelligent interface that infers movement intentions by deciphering the electrical activity of muscles, known as the surface electromyogram (EMG) signal. Despite encouraging demonstrations, translation of this research into the clinic has been limited (Jiang, Dosen, Müller, & Farina, 2012; Kuiken et al, 2014). This shortcoming may be because it is very difficult for the amputees to generate distinct activity patterns for different movement classes of hand or wrist movements. Because it is not clinically viable to emulate all possible variations, during prosthesis use, incorrect classifications will likely take place in response to EMG patterns that were not observed during training or recalibration
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