Paper Info
Title
Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.
Abstract
So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training 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 gestures dominated 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 hand exoskeleton control.
Year
DOI
Venue
2018
10.3389/fnbot.2018.00003
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
myoelectric control,training set,similar hand gestures,sample proportion,pattern recognition
Feature vector,Zero crossing,Pattern recognition,Computer science,Gesture,Support vector machine,Gesture recognition,Exoskeleton,Artificial intelligence,Classifier (linguistics),Fist,Machine learning
Journal
Volume
ISSN
Citations 
12
1662-5218
1
PageRank 
References 
Authors
0.35
16
9
Name
Order
Citations
PageRank
Yao Zhang16631.44
Yanjian Liao210.35
X Y Wu322.12
Lin Chen410.35
Qiliang Xiong510.35
Zhi-Xian Gao631.43
Xiaolin Zheng794.37
Guanglin Li831457.23
Wen-Sheng Hou97813.04