A Data-Fusion Algorithm for Respiration Rate Extraction Based on UWB Transversal Propagation Method

This paper presents a data-fusion algorithm for respiration rate (RR) extraction by employing the ultra-wideband (UWB) transversal propagation measurement method. In the experimental scenario the UWB transmitter and receiver units were placed on the front and back sides of the thoracic wall. The measurement principle is based on the fact that periodic movements of lung affect the communication channel properties that can be measured. We measured the energy attenuation variations on the receiver side caused by tissue movements from which we extracted the information about the respiration rate. For testing purpose, a custom developed UWB platform suitable for on-body placement was used, with an integrated inertial motion unit (IMU) sensor. UWB and IMU signals were combined by means of a data fusion algorithm. Data fusion algorithms based on Extended Kalman filtering (EKF) and Naïve Bayes inference show better estimation performance than an estimation from individual signal sources. The obtained error rate of RR estimation by means of the proposed data fusion method is lower than 0.2 respiration per minute (rpm) in comparison to the reference respiration system. Our results show that the proposed method of UWB and IMU sensor fusion is a promising candidate for reliable RR monitoring by compact wearable units under relatively non-stationary body movement conditions.