An assistive technology (AT) in the form of a novel human interface device (HID) using (electromyography) EMG-based gesture recognition scheme is proposed in this thesis. The system utilizes MYO device from Thalmic Labs with a custom software as the signal capturing tool. The electrodes of the EMG are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a Hidden Markov model (HMM) algorithm to classify the gesture, and based on the gesture a custom command can be assigned. Depending on the gesture made, there are countless possible command. The accuracy of the system is 94.4% with 5 gestures classification

An assistive technology (AT) in the form of a novel human interface device (HID) using (electromyography) EMG-based gesture recognition scheme is proposed in this thesis. The system utilizes MYO device from Thalmic Labs with a custom software as the signal capturing tool. The electrodes of the EMG are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a Hidden Markov model (HMM) algorithm to classify the gesture, and based on the gesture a custom command can be assigned. Depending on the gesture made, there are countless possible command. The accuracy of the system is 94.4% with 5 gestures classification

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