Effectiveness of surface electromyography in pattern classification for upper limb amputees

This study was undertaken to explore 18 time domain (TD) and time-frequency domain (TFD) feature configurations to determine the most discriminative feature sets for classification. Features were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements and used to perform a series of classification trials with the random forest classifier. Movement datasets for 11 intact subjects and 9 amputees from the NinaPro online database repository were used. The aim was to identify any optimum configurations that combined features from both domains and whether there was consistency across subject type for any standout features. This work built on our previous research to incorporate the TFD, using a Discrete Wavelet Transform with a Daubechies wavelet. Findings report configurations containing the same features combined from both domains perform best across subject type (TD: root mean square (RMS), waveform length, and slope sign changes; TFD: RMS, standard deviation, and energy). These mixed-domain configurations can yield optimal performance (intact subjects: 90.98%; amputee subjects: 75.16%), but with only limited improvement on single-domain configurations. This suggests there is limited scope in attempting to build a single absolute feature configuration and more focus should be put on enhancing the classification methodology for adaptivity and robustness under actual operating conditions.