Abstract
Various intelligent home control systems have been extensively studied and applied. However, few researches focused on intelligent home control based on physiological signal recognition. The intelligent home control system is designed based on physiological signals such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG). A EEG wearable device designed, which uses the TGAM (ThinkGear AM) Brain Wave Sensor Chip to collect and preprocess signals. The Raspberry Pi extracts the control signals. In addition, the STM32 controls the intelligent home sand table model. Besides, the Bluetooth communication and serial communication are used to realize a split design. A combination of Wavelet Transform (WT) and Fourier Transform (FT) is proposed to extract the features of the control signals. An effective method for improving the accuracy of EMG signals classification is Support Vector Machine (SVM) with linear kernel (Linear SVM). Therefore, EMG signals are recognized by Linear SVM classification. This system is stable, accurate and easy to operate, which can be widely used in the field of new smart home.
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