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Most smart home products on the market today only stay at the level of multi-channel control of home appliances and have a single product function. For those parts that are not involved, this article describes a smart home cloud system based on Kinect. The system uses the STM32 as the control core and utilizes Kinect depth sensors to establish an identity and posture recognition system based on human skeletons, combined with support vector machine (SVM) and Internet of Things technology to realize smart home control systems. What is more, intelligent alarm system and child anti-drop window control system truly realize home Intelligence. After verification, the system can greatly improve the current level of intelligent home security capabilities, make up for the vacancy of the existing identity system, real intelligence for the home rather than intelligent control of home appliances is finally achieved.

Deep learning approaches have been used successfully in computer vision, natural language processing and speech processing. However, the number of studies that employ deep learning on brain-computer interface (BCI) based on electroencephalography (EEG) is very limited. In this paper, we present a deep learning approach for motor imagery (MI) EEG signal classification. We perform spatial projection using common spatial pattern (CSP) for the EEG signal and then temporal projection is applied to the spatially filtered signal. The signal is next fed to a single-layer neural network for classification. We apply backpropagation (BP) algorithm to fine-tune the parameters of the approach. The effectiveness of the proposed approach has been evaluated using datasets of BCI competition III and BCI competition IV.