Research on Human ADL Data Real-Time Transmission Optimization Method Based on Bayesian Network

Abstract

A new design method of human-body ADL(activity of daily living) real-time monitoring based on Internet of things is proposed, which is able to detect body posture of elderly persons and biological signal at rehabilitation centers or nursing homes. With this way patients body state information can be transmitted to doctors or their family through mobile phone or PC. Human body wear sensor nodes which consist of wearable sensors and data transmission module to detect posture or bodys activity and transmit data to base station which is distributed in elder persons daily living environments. It's possible that increased number of nodes in each base station may cause network congestion. Real-time performance in this data transmission system is important to protect elder people when abnormal activities occur. A new data processing algorithm, which can reduce the transmission cycle time effectively and improve the real-time and robustness performance, based on Bayesian network is presented here. Finally, experimental results indicate that Bayesian network parameters training method is effective and real-time performance is improved.