Dual Stage Classification of Hand Gestures using Surface Electromyogram
This work addresses gesture recognition for applications like human-machine interfaces and prosthetics, but it appears incremental as it builds on existing classification methods with a grouping strategy.
The paper tackles the problem of classifying hand gestures from surface electromyogram (sEMG) signals by proposing a dual-stage classification approach that groups similar gestures and then classifies within groups, resulting in significantly higher classification accuracies compared to conventional single-stage methods.
Surface electromyography (sEMG) is becoming exceeding useful in applications involving analysis of human motion such as in human-machine interface, assistive technology, healthcare and prosthetic development. The proposed work presents a novel dual stage classification approach for classification of grasping gestures from sEMG signals. A statistical assessment of these activities is presented to determine the similar characteristics between the considered activities. Similar activities are grouped together. In the first stage of classification, an activity is identified as belonging to a group, which is then further classified as one of the activities within the group in the second stage of classification. The performance of the proposed approach is compared to the conventional single stage classification approach in terms of classification accuracies. The classification accuracies obtained using the proposed dual stage classification are significantly higher as compared to that for single stage classification.