Efficient Hand Movement Detection Using k-Means Clustering and k-Nearest Neighbor Algorithms

• Published in: Journal of medical and biological engineering Vol. 41; no. 1; pp. 11 - 24
• Main Authors: Bergil, Erhan, Oral, Canan, Ergul, Engin Ufuk
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021; Springer Nature B.V
• Subjects: Algorithms; Approximation; Biomedical Engineering and Bioengineering; Cell Biology; Classification; Cluster analysis; Clustering; Electromyography; Engineering; Entropy; Feature extraction; Females; Gender; Imaging; Males; Motion detection; Motion perception; Original Article; Principal components analysis; Prostheses; Radiology; Vector quantization; Wavelet transforms; Prosthetic limb; Clustering; EMG; Hand movements; Classification
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-020-00537-4

Purpose Electromyography (EMG) signals are commonly used in prosthetic limb studies. We have proposed a system to detect six basic hand movements using unsupervised and supervised classification algorithms. In this study, two-channel EMG recordings belonging to six different hand movements are analyzed and the performance of the wavelet-based features for hand movement clustering and classification are examined for six subjects (three females and three males). Methods The approximation and detail components are obtained by four-level symmetric wavelet transform. The energy, mean, standard deviation, and entropy values of the wavelet components are calculated and the feature sets are generated. After feature extraction, feature set dimensionality is reduced using principal component analysis, and then the k-nearest neighbor method and k-means clustering are applied for classification and clustering, respectively. The analyses are performed subject-specifically and gender-specifically. Thus, it is possible to evaluate the gender effect on classification performances. Results Subject-specific hand movements were detected with accuracy in the range of 86.33–100%. Gender-specific hand movements were detected with an accuracy of 96.67% for males and 92.78% for females. Conclusions The classification and clustering results support each other. It was observed that the samples of hand movements that were classified incorrectly were concentrated in the same clusters. Similarly, it was found that the hand movements that were easily detected were homogeneously clustered.