Abstract
In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.
Highlights
Hand or arm gestures are types of nonverbal communication along with facial expressions
We have described a hand gesture recognition method using EMG and accelerometer signals simultaneously recorded from wrist area to overcome the degradation of performance according to various arm postures
Changes of EMG signals due to arm postures have been reported in various literatures, and many researchers have tried to solve the issue by using accelerometer signals
Summary
Hand or arm gestures are types of nonverbal communication along with facial expressions. Hand gestures are the most frequent type of nonverbal communication and represent diverse intentions. Vision-based studies that employ camera sensors such as time-of-flight (ToF) or RGB cameras[1,2] were conducted and progressed to improve the recognition accuracy of hand gestures by incorporating the depth and color of images. Those studies are vulnerable to surrounding brightness and have a limitation in recognizing hand gestures within the sight of the camera sensors. There have been studies that employed accelerometer, gyroscope, magnetoresistive, and flexure sensors embedded in data gloves to track hand motions or to recognize hand gestures.[3,4]
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