We use the reflected signal from the passive tag to enable recognition of human motions, which can count the repetitive motion of human body. We design an adaptive counting algorithm, and use the dynamic time wrap (DTW) method to stretch the wireless signal on the time series for comparing. The counting accuracy has nothing to do with the experimental environment due to the adaptive algorithm, the average count error is controlled within 5%, and by using the periodicity of repetitive motions，we can further recognize the types of motions with an accuracy more than 95%.
This work is about wireless charging, which is a combination of hardware circuits and software issues. If the current in the transmitter coil changes, the coil will produce a change in the magnetic field. If the magnetic field changes, the pounded coil will generate electrical energy. By changing the current phase of the load feedback, the controller adjusts the current at the transmitter to maximize the transmission power. Power through the air transmission, which frees people to use the space constraints of electricity, wireless charging has great prospects for development.
In order to come out with a cost-effective home monitoring system, our team begin to turn attention to the commodity Wi-Fi devices already installed in the home for contact-free vital sign measurement. More robust and reliable solutions resort to finer- grained channel descriptor at physical layer that is more sensitive to human presence while keeps rather stable in static environments. Exploiting full information (both amplitude and phase) provided by CSI, our approach is able to accurately detect human movements and so on.