Classification of Gait Phases Based on Bilateral EMG Data Using Support Vector Machines

被引：0

作者：

Ziegler, Jakob ^{[1
]}

Gattringer, Hubert ^{[1
]}

Mueller, Andreas ^{[1
]}

机构：

[1] Johannes Kepler Univ Linz, Inst Robot, A-4040 Linz, Austria

来源：

2018 7TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB2018) | 2018年

关键词：

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Robotic systems for rehabilitation of movement disorders and motion assistance are gaining increased attention. Robust classification of motion data as well as reliable recognition of the user's intended movement play a major role in order to maximize wearability and effectiveness of such systems. Biological signals like electromyography (EMG) provide a direct connection to the motion intention of the wearer. This paper addresses the classification of stance phase and swing phase during healthy human gait based on the muscle activity in both legs using the theory of Support Vector Machines (SVM). A novel EMG feature calculated from the bilateral EMG signals of muscle pairs is introduced. The presented method shows promising results with classification accuracies of up to 96%.

引用

页码：978 / 983

页数：6

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