Determination of Optimum Segmentation Schemes for Pattern Recognition-Based Myoelectric Control: A Multi-Dataset Investigation

Abstract

Pattern recognition (PR) algorithms have shown promising results for upper limb myoelectric control (MEC). Several studies have explored the efficacy of different pre and post processing techniques in implementing PR-based MECs. This paper explores the effect of segmentation type (disjoint and overlap) and segment size on the performance of PR-based MEC, for multiple datasets recorded with different recording devices. Two PR-based methods; linear discriminant analysis (LDA) and support vector machine (SVM) are used to classify hand gestures. Optimum values of segment size, step size and segmentation type were considered as performance measure for a robust MEC. Statistical analysis showed that optimum values of segment size for disjoint segmentation are between 250ms and 300ms for both LDA and SVM. For overlap segmentation, best results have been observed in the range of 250ms-300ms for LDA and 275ms-300ms for SVM. For both classifiers the step size of 20% achieved highest mean classification accuracy (MCA) on all datasets for overlap segmentation. Overall, there is no significant difference in MCA of disjoint and overlap segmentation for LDA (P-value = 0.15) but differ significantly in the case of SVM (P-value < 0.05). For disjoint segmentation, MCA of LDA is 88.68% and for SVM, it is 77.83%. Statistical analysis showed that LDA outperformed SVM for disjoint segmentation (P-value < 0.05). For overlap segmentation, MCA of LDA is 89.86% and for SVM, it is 89.16%, showing that statistically, there is no significant difference between MCA of both classifiers for overlap segmentation (P-value = 0.45). The indicated values of segment size and overlap size can be used to achieve better performance results, without increasing delay time, for a robust PR-based MEC system.

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

“…The results of this study related to motions classification and the association between level of motor impairment and its effect on performing ankle joint movements are summarized in Table 2 and in Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 . As studies have reported differences in EMG signals on the basis of sex, age, and anthropometric variables; therefore, the results of this study are not representative of the whole population [ 33 ].…”

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

“…After getting the digital EMG signal through the process in Figure 2(a) or from dataset 2, we passed the EMG signal Computational Intelligence and Neuroscience through a digital preprocessing block using MATLAB R2017a environment where a digital bandpass filter (20 to 500 Hz) and a digital notch filter (50 Hz) were used to reduce movement artefacts, high-frequency noise [63], and power line artefacts [69]. In general, two types of windowing, i.e., overlapped and disjoint windowing, are used [70]; however, between these two, the overlapped windowing scheme offers better pattern recognition performance, but its computational cost is higher [71].…”

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

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