A Remote Health Detection System with Sensor Signal Purification

In order to relieve the pressure of medical resources and meet the demand of human health real-time detection, a remote health detection algorithm based on sensor signal purification was designed. In the wearable health device, health parameter sensors such as integrated temperature sensors were set up, and the results of human health sensing signals were collected by wireless communication technology and transmitted to a remote data processing center. The data processing center used the wavelet threshold method to denoise the human health sensing signal, and selected the joint time–frequency decomposition method to purify the human health sensing signal. The purified result of the sensor signal was set as the input sample of the hidden Markov model. The Baum-Welch local optimization algorithm was used to update the parameters of the hidden Markov model until the posterior probability of the model approached the maximum value, and the human health detection results were output. The experimental results show that the acquisition error of human physiological parameters is less than 0.5% when the sensor signal is purified by this algorithm, and the abnormal health state of testers such as falling, high body temperature and high blood pressure is accurately detected.