13:30 - 15:00 | Tue 30 Oct | Ambassador A | B4L-B
In this work, we present a novel set of higher order time domain features for surface electromyographic (EMG) pattern recognition. The proposed methods employ simple measures of frequency information extracted from EMG time series when a sequence of differencing filters is applied. Multiple EMG datasets consisting of 48 able-bodied and transradial amputee subjects performing a large variety of hand and fingers movements are used to evaluate the performance and robustness of the proposed features. The results show that these novel higher order-based features provide significantly better performance than their traditional counterparts by 3-15% (p < 0:05). The best proposed feature, higher-order myopulse percentage rate, also significantly outperformed other frequency information-based EMG features in the time and frequency domains: histogram, mean frequency, and median frequency, by 8-14%, 8-25%, and 14-35% (p < 0:05), respectively. With relatively less computational complexity, the proposed features could potentially be used as new features for extracting frequency information for EMG- based pattern recognition systems.
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