Abstract
In this paper, we propose a system for inferring the pinch-to-zoom gesture using surface EMG (Electromyography) signals in real time. Pinch-to-zoom, which is a common gesture in smart devices such as an iPhone or an Android phone, is used to control the size of images or web pages according to the distance between the thumb and index finger. To infer the finger motion, we recorded EMG signals obtained from the first dorsal interosseous muscle, which is highly related to the pinch-to-zoom gesture, and used a support vector machine for classification between four finger motion distances. The powers which are estimated by Welch's method were used as feature vectors. In order to solve the multiclass classification problem, we applied a one-versus-one strategy, since a support vector machine is basically a binary classifier. As a result, our system yields 93.38% classification accuracy averaged over six subjects. The classification accuracy was estimated using 10-fold cross validation. Through our system, we expect to not only develop practical prosthetic devices but to also construct a novel user experience (UX) for smart devices.
Highlights
Gesture recognition is one of the most interesting research areas because of its utility in the human computer interface (HCI) field
Many researchers have tried to construct a hand and finger gesture recognition system based on the surface electromyogram, which detects the motor unit action potential (MUAP) derived from different motor units during muscle contraction [3]
Even though many researchers have focused on recognizing the hand movement, finger movement based on the surface electromyogram (sEMG), has been studied because of its potential utilization in HCI and prosthetic devices
Summary
Gesture recognition is one of the most interesting research areas because of its utility in the human computer interface (HCI) field. Even though many researchers have focused on recognizing the hand movement, finger movement based on the sEMG, has been studied because of its potential utilization in HCI and prosthetic devices. Al-Timemy et al used time domain-autoregression feature and orthogonal fuzzy neighborhood discriminant analysis for recognizing finger movements based on sEMG. They showed that the abduction of finger and thumb movements can be successfully classified with few electrodes [14]. Some researchers devised wearable devices such as arm- and wristbands which recognize the finger gestures Based on their wearable systems, they developed applications to control music players, games and interpret sign language [15,16,17].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
