Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.

	Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.
	Wu, Xiaoying; Zhang, Yao; Liao, Yanjian; Chen, Lin; Xiong, Qiliang; Gao, Zhixian; Zheng, Xiaolin; Li, Guanglin; Hou, Wensheng
刊名	FRONTIERS IN NEUROROBOTICS
	2018
文献子类	期刊论文
英文摘要	So far, little is known how the sample assignment of surface electromyogram (sEMG) features intraining set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gesturesdominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 +/- 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic handexoskeleton control.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14160]
专题	深圳先进技术研究院_医工所
推荐引用方式 GB/T 7714	Wu, Xiaoying,Zhang, Yao,Liao, Yanjian,et al. Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.[J]. FRONTIERS IN NEUROROBOTICS,2018.
APA	Wu, Xiaoying.,Zhang, Yao.,Liao, Yanjian.,Chen, Lin.,Xiong, Qiliang.,...&Hou, Wensheng.(2018).Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities..FRONTIERS IN NEUROROBOTICS.
MLA	Wu, Xiaoying,et al."Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.".FRONTIERS IN NEUROROBOTICS (2018).