An Investigation of Temporally Inspired Time Domain Features for Electromyographic Pattern Recognition.

This paper presents a novel set of temporally inspiredtime domain features for electromyographic (EMG) pattern recognition. The proposed methods employ simple time series measures derived from peak detection, and could better reflect EMG activity over time. Multiple EMG datasets consisting of 68 able-bodied and transradial amputee subjects performing a large variety of hand, wrist, fingers, and grasping movements are used to evaluate the performance of the proposed features and to design robust EMG feature sets. The results show that the average classification accuracy of two novel features, the mean prominence of local peaks and valleys, outperform several commonly used time domain features, autoregressive coefficients, histogram, and zero crossing, by 8{\%, 11{\%, and 17{\%, respectively. The proposed features are also shown to provide additional information as part of a robust feature set when compared to the common Hudgins' timedomain feature set, as selected by sequential forward selection and through empirical feature set design.