On-body propagation characterization based on FDTD method for 2.4/5.2/5.7 GHz wearable body sensor networks

The on-body path loss and time delay of radio propagation in 2. 4/5.2/5.7 GHz wearable body sensor networks （W-BSN） are studied using Remcom XFDTD, a simulation tool based on the finite-difference time- domain method. The simulation is performed in the environment of free space with a simplified three- dimensional human body model. Results show that the path loss at a higher radio frequency is significantly smaller. Given that the transmitter and the receiver are located on the body trunk, the path loss relevant to the proposed minimum equivalent surface distance follows a log-fitting parametric model, and the path loss exponents are 4. 7, 4. 1 and 4. 0 at frequencies of 2. 4, 5.2, 5.7 GHz, respectively. On the other hand, the first- arrival delays are less than 2 ns at all receivers, and the maximum time delay spread is about 10 ns. As suggested by the maximum time delay spread, transmission rates of W-BSN must be less than 10^8 symbol/s to avoid intersymbol interference from multiple-path delay.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

Design and Implementation of a Wearable Body Area Sensor Network for Distributed FES System

A wearable body area sensor network(WBASN) was designed and implemented to monitor movement information of stroke patients in real time. The sensor system was combined with a previously developed distributed functional electrical stimulation(d FES) system, which is a promising technology for motor rehabilitation of stroke patients. Movement information could be useful in outcome assessment of rehabilitation, or for closed-loop adaptive stimulation during rehabilitation. In addition,a short-latency, low-power communication protocol was developed to meet the clinical requirements of energy efficiency and high rate of data feed-through. The prototype of the WBASN was tested in preliminary human experiments. Experimental results demonstrate the feasibility of the proposed wearable body area sensor network in monitoring arm movements on healthy subjects.