oa Low-Complexity Wireless Monitoring Of Respiratory Movements Using Ultra-Wideband Impulse Response Estimation

Monitoring of respiratory rate and amplitudes of a human being has important medical applications such as in health monitoring as well as in diagnosis of respiratory illnesses and disorders such as the sleep apnea disorder. Currently used techniques for respiratory monitoring require patients to wear sensors and electrodes across the chest to measure the respiratory effort. Such techniques not only cause discomfort to the patient but also require expert supervision. It is desirable to have a low-cost wireless monitoring equipment that is able to measure the respiratory rate and amplitudes through a non-contact and non-invasive methodology. Ultra-wideband (UWB) technology offers promising features for wireless monitoring in medical environments. However, there are several challenges that need to be addressed to enable development of a cost effective and reliable product capable of operating in realistic environments. Such challenges include low signal-to-noise ratios (SNR), interference from moving background objects (clutters) and the need for expensive sampling equipment. We propose a comprehensive scheme for wireless monitoring of the respiratory movements using UWB technology. Our scheme overcomes all the three challenges mentioned above. The proposed scheme is based on the estimation of the ultra-wideband channel impulse response. We exploit the sparsity inherent in the UWB channel to estimate the respiratory movements while operating in a Bayesian framework. We suggest techniques for dealing with background clutter in situations when it might be time variant. We also present a novel methodology for reducing the required sampling rate of the system significantly while achieving the accuracy offered by the Nyquist rate. Results from simulations conducted with pre-recorded respiratory signals demonstrate the robustness of our scheme for tackling the above challenges and providing a low-complexity solution for the monitoring of respiratory movements in harsh operating environments. Equipment based on our technique will be particularly useful to hospitals for clinical diagnosis purposes and also for commercial household usage by people in need of continuous monitoring of their health status. Measurements acquired through the equipment can potentially be communicated to a remote terminal/station for analysis by physicians. The equipment will also be useful in search and rescue scenarios for detecting and monitoring the vital signs of survivors trapped under debris and rubble in disaster management scenarios as well as in surveillance operations such as detecting people behind a wall.