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A Body Sensor Network can sense health parameters directly on the patient’s body, allowing 24/7 monitoring in an unobtrusive way. Several tiny sensors collect and route data to a special sink node. A new intra-vaginal biosensor was developed to study the relation between temperature variations and women health conditions, such as ovulation period, among others. We present a biosensor prototype and some initial results on real scenarios with a woman. One of the main issues in a body sensor network is the transformation of the sensor raw data into meaningful medical data for medical staff. Several approaches exist, from mobile device-based approaches to more powerful hardware such as a personal computer. This paper presents our current work in body sensor networks, namely a prototype for intra-vaginal temperature monitoring with initial results, and a mobile tool for data presentation of a three-tier body sensor network. The gathered results demonstrate the feasibility of the approach, contributing to the widespread application of body sensor networks.

This work focuses on the evaluation of blind sensing techniques for the detection of multiple wireless microphones in the UHF band, by means of simulation. The metrics used for the comparisons include probability of detection, probability of false alarm and minimum SNR detected for a given observation time. As an example, simulation results showed that blind detection algorithms can sense multiple wireless microphone signals with SNR = -19 dB, in a Rayleigh channel environment, considering 100 ms sensing time, 90 % probability of detection and 10 % probability of false alarm. In these conditions, blind detection techniques suffer maximum SNR degradation of 3.5 dB, as compared with single wireless microphone scenarios.