Abstract
Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.
Highlights
Pattern recognition-based myoelectric control of externally powered prostheses has demonstrated remarkable potential to restore function to individuals with upper-limb amputations
Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees
For non-amputees, performance was comparable across classifiers, except that the quadratic discriminant analysis classifier (QDA) performed significantly worse than all other classifiers
Summary
Pattern recognition-based myoelectric control of externally powered prostheses has demonstrated remarkable potential to restore function to individuals with upper-limb amputations This control method has shown promise in laboratory settings (Kuiken et al, 2009; Scheme and Englehart, 2011), and a pattern recognition myoelectric controller is clinically available for individuals with high-level upper-limb amputations (Uellendahl et al, 2016). This population comprises less than 10% of all upperlimb amputations in the United States (Dillingham et al, 2002; Ziegler-Graham et al, 2008). Partial-hand amputees perceive themselves to be at a higher disability level than do individuals with unilateral transradial or transhumeral amputations (Davidson, 2004; McFarland et al, 2010), they are more likely to reject their prosthesis (Biddiss and Chau, 2007), and more than half are unable to return to their previous occupation (Burger et al, 2007)
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