Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities - Zhang, Yao; Liao, Yanjian; Wu, Xiaoying; Chen, Lin; Xiong, Qiliang; Gao, Zhixian; Zheng, Xiaolin; Li, Guanglin; Hou, Wensheng (Frontiers in Neurorobotics, 2018)
So far, little is known how the sample assignment of sEMG (surface electromyogram) 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 seconds and followed by a five-second 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-second sEMG from each channel was segmented for EMD (Empirical Mode Decomposition) analysis trial by trial. The muscle activity was quantified with ZC (zero crossing) and WAMP (Willson amplitude) of the first four resulting IMFs (Intrinsic Mode Function). Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with SVM (Support Vector Machine) and GA (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.