Camera control with EMG signals using Principal Component Analysis and support vector machines

The main goal of Human Computer Interface (HCI) is to improve the interactions between users and computers by making computers more usable and receptive to the user's needs. Accordingly, surveillance is one of the major areas where human computer interface is critical. Surveillance cameras are usually controlled with joysticks. For this reason, it is almost impossible to be controlled by an amputee with no finger functionality. In this paper, the Fast Fourier Transform (FFT) analysis was applied to raw EMG data and then features are extracted with Principal Component Analysis (PCA) and Simple Principal Component Analysis (SPCA). In the proposed system, in order to make a decision whether the wrist is moving right, left, up, down or neutral, multi-class Support Vector Machine is employed. Additionally to Electromyography (EMG) signals, standard datasets that involves Electroencephalography (EEG) signals is also tested with multi-class SVM to verify the system robustness. Finally, classified EMG decisions are received by the camera as movement comments. Successful operation of camera employing EMG signals has been accomplished with 81% accuracy with SPCA.