Intelligent remote control of smart home devices using physiological parameters

The goal of this paper is to propose a new approach for controlling smart home devices using physiological and movements signals (electromyogram, accelerometer and gyroscope). Our proposed approach exploits the ElectroMyoGram (EMG) signal to detect user muscles contraction and to trigger the associated actions on the controlled device. The triggered action is based on the position of the hand or its movement pattern. The Support Vector Machine (SVM) is used to classify the gyroscope data and to detect hand movements. The position of the hand is determined using the K-Nearest Neighbors (KNN) algorithm. Our proposed approach can be used to command any electronic device or connected objects and it is intended to work with data from wearable arm band containing triaxial accelerometer, gyroscope and able to measure EMG signal. Our experimental results are very encouraging where we achieve fast processing and reliable evaluation of hand movement with very low rate of actions miss interpretation.