论文标题
使用表面肌电图的手势双重分类
Dual Stage Classification of Hand Gestures using Surface Electromyogram
论文作者
论文摘要
表面肌电图（SEMG）在涉及人类运动的分析（例如人机界面，辅助技术，医疗保健和假肢发展）的应用中变得越来越有用。拟议的工作提出了一种新型的双阶段分类方法，用于分类SEMG信号的手势。提出了对这些活动的统计评估，以确定所考虑活动之间的相似特征。类似的活动被分组在一起。在分类的第一阶段，将活动确定为属于一个组，然后在分类的第二阶段进一步将其归类为组中的活动之一。根据分类精度，将提出方法的性能与常规的单阶段分类方法进行了比较。与单阶段分类相比，使用建议的双阶段分类获得的分类精度明显更高。
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.
