A Multi-modal Health Data Fusion and Analysis Method Based on Body Sensor Network

As an important branch of wireless sensor network (WSN) in biomedical field, body sensor network (BSN) could remotely monitor a variety of human health data in real time by means of energy-saving and high-accuracy sensor technology. We proposed a multi-modal health data fusion and analysis method based on the data collected from BSN. Single-modal Holter monitoring and multi-modal health monitoring were performed on 60 patients with confirmed heart disease and proved that the proposed method could effectively improve the detection rate of asymptomatic myocardial ischemia and provide a new auxiliary judgment method for clinical application. Aiming at the requirement of privacy preserving in the data fusion process of wireless body sensor network, a new SMART-RR algorithm was proposed. Simulation results showed that MART-RR was an energy-saving privacy preserving data fusion algorithm with small data communications, high privacy protection and accuracy.