Health assessment method based on multi-sign information fusion of body area network

The widespread application of technologies such as the Internet of Things (IoT) and wireless sensors has promoted the development of body area networks (BAN) in the area of intelligent monitoring. However, current health assessment methods based on BAN still have problems such as a high false alarm rate and low efficiency in identifying signs and states, which not only increase the psychological burden of the ward but also bring unnecessary troubles to the medical staff. In response to this problem, this paper proposes a multi-sign parameter fusion health assessment model based on BP neural network (BPNN). Firstly, the blood pressure, heart rate, pulmonary hypertension, respiration rate, blood oxygen, and body temperature are obtained by sensors in real-time, and then these six parameters are fused by the BPNN. In addition, aiming at the problems of slow convergence speed and easy falling into a local minimum in BPNN, the structure of this model is optimized, and the influence of the number of neurons and activation function of the hidden layer on the performance of the model is explored. Results show that when the number of neurons in the hidden layer is 13 and the activation function is Logsit, the performance of the model is optimal. Among them, the recognition accuracy of the model is 95 %, and the running time is 2.798 s. Finally, comparing the recognition results of this model with support vector machines (SVM), genetic BP neural networks (GA-BPNN), and fuzzy neural networks (FNN), it is found that the accuracy of these three methods is 70 %, 70 % and 80 % respectively, which verifies the validity of the model proposed in this paper.