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This paper presents a wearable sensor patch with real-time respiration monitoring by measuring the change in thoracic impedance resulting from breathing. A bioimpedance (BioZ) sensor with two sensing electrodes is employed to measure the chest impedance. In addition, a medical-grade infrared temperature sensor is utilized to detect body temperature. The recorded data is transmitted via a Bluetooth module to a computer for online data computation and waveform visualization. The breath-by-breath breathing rate is calculated using the time difference between two BioZ signal peaks, and the results are validated against a commercial respiration monitoring belt. Experimental tests have been conducted on five subjects in both static (i.e., sitting, supine, sleeping on the left side, sleeping on the right side, and standing) and dynamic (i.e., walking) conditions. The experiment measurements show that the BioZ sensor patch can be used to monitor the breathing rate accurately in static conditions with a low mean absolute error (MAE) of 0.71 breath-per-minute (bpm) and can detect breathing rate effectively in a dynamic environment as well. The results suggest the feasibility of using the proposed approach for respiration monitoring in daily life.

This paper proposes a wireless wearable sensor system for the continuous beat-to-beat blood pressure (BP) monitoring. The real-time BP can be estimated utilising a 2-parameter regression model based on the pulse arrival time (PAT) and heart rate (HR). The PAT is defined as the time interval between the electrocardiogram (ECG) R-peak and the corresponding maximum inclination point of photoplethysmography (PPG) signal. A wireless wearable sensor patch designed to be attached to the subject's chest is used for the measurement of ECG and PPG signals. The sensor data are transmitted through a Bluetooth low energy (BLE) module to a computer for the real-time online estimation of BP. To verify the feasibility and performance of the proposed system, a 5-day period experiment is conducted on two healthy male subjects for the training and validation of the BP estimation model. On each day, there are two 15 minutes offline sessions for data collection from the sensor patch, which are compared with the reference BP to calibrate the estimation model parameters. After that, a 10 minutes online session is carried out to validate the regression model against the reference BP device. Eventually, the 5-day period data are combined together for an overall BP estimation model, which has good correlation (r=0.82) with the reference BP measurements. The experimental results show the proposed sensor patch with the BP estimation model is capable of the online real-time BP monitoring after an initial calibration procedure.