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This study addresses two critical aspects of gesture classification: data acquisition and feature extraction. It introduces an efficient surface electromyogram (sEMG) acquisition band with seven active electrodes and conducts experiments with eight healthy subjects for six gestures. A novel feature extraction method, the Hjorth secant line (HSL), is also introduced. The self-generated dataset (DB1) and two publicly available datasets (DB2 and DB3), containing data from both healthy subjects and amputees, are analyzed to assess the proposed feature set’s performance. The study evaluates the sEMG acquisition system’s effectiveness by measuring the signal-to-noise ratio (SNR), and the performance of the proposed feature set was evaluated in terms of gesture classification accuracy. The feature set’s performance is compared to other existing feature sets in the literature. In addition, a time complexity analysis is performed for the proposed feature set. The SNR for the sEMG acquisition system is 49. 36 ± 5. 50 dB, demonstrating its efficiency. When using the proposed feature set with a random forest (RF) classifier, the study achieves the classification accuracy of 97. 94% ± 0. 47%, 98. 94% ± 0. 46%, and 82. 16% ± 2. 07% for DB1–DB3, respectively. Paired t -tests indicate that the proposed feature set significantly improves gesture classification accuracy across all three datasets (p -value < 0. 05). The time required to calculate the proposed features was 0. 207 ms, which is shorter than the computational time for other feature extraction methods reported in the literature. This study highlights the enhanced SNR from the proposed acquisition system and the feature set’s potential to improve gesture classification. These findings have substantial implications for advancing intelligent prosthetics.
Rani et al. (Wed,) studied this question.