School of Mechanical Engineering, Hefei University of Technology, 230009, Hefei, People's Republic of China. State Key Laboratory of Robotics and Systems (HIT), 150001, Harbin, People's Republic of China. Author to whom any correspondence should be addressed.
J Neural Eng. 2020 Jul 24;17(4):046016. doi: 10.1088/1741-2552/ab9db9.
Human intention gesture recognition is widely used in hand rehabilitation, artificial limb control, teleoperation, human-computer interaction and other fields. It has great application value, however, how to extract human intention gesture accurately has been a research hotspot.
Inspired by the image processing technology of machine vision, the surface electromyographic (sEMG) signal was selected as the source signal of motion intention in this work, and the original sEMG signal was converted into Gramian Angular Summation/Difference Field (GASF/GADF) image. Then, Histogram of Oriented Gradient (HOG) features of the corresponding GADF and GASF image were extracted. The extracted features are named as GASF-HOG and GADF-HOG. The Bagging method was used to map the features to six common gestures to realize the classification of intention gestures. Ten volunteers participated in the experiment, and the experimental data were used to verify the proposed method.
The experimental results showed that the average accuracies of the proposed methods (GADF-HOG with Bagging, GASF-HOG with Bagging) were as follow: GADF-HOG with Bagging was with 95.73 ± 1.90%, and GASF-HOG with Bagging was with 93.63 ± 1.54%.
The method proposed in this paper is inspired by image processing technology of machine vision, which provides a new idea about the human intention gesture recognition by combining the interdisciplinary knowledge.
人类意图手势识别广泛应用于手部康复、假肢控制、遥操作、人机交互等领域，具有重要的应用价值，如何准确提取人类意图手势一直是研究热点。
受机器视觉图像处理技术的启发，本研究选择表面肌电（sEMG）信号作为运动意图的源信号，将原始 sEMG 信号转换为 Gramian Angular Summation/Difference Field（GASF/GADF）图像。然后，提取相应 GADF 和 GASF 图像的 Histogram of Oriented Gradient（HOG）特征。提取的特征分别命名为 GASF-HOG 和 GADF-HOG。采用 Bagging 方法将特征映射到六个常见手势，实现意图手势的分类。十位志愿者参与了实验，使用实验数据验证了所提出的方法。
实验结果表明，所提出方法（Bagging 下的 GADF-HOG 和 Bagging 下的 GASF-HOG）的平均准确率分别为：Bagging 下的 GADF-HOG 为 95.73±1.90%，Bagging 下的 GASF-HOG 为 93.63±1.54%。
本文提出的方法受到机器视觉图像处理技术的启发，通过结合跨学科知识，为人类意图手势识别提供了新的思路。
