SeismoNet: A Multi-Node Wireless Wearable Platform for Enhanced Physiological Sensing

Continuous remote monitoring of key health parameters can be facilitated by noninvasive cardiovascular signals such as the seismocardiogram (SCG) and electrocardiogram (ECG). However, the accuracy of health parameter estimation is dependent on the quality of the signals collected and the algorithms used, which can be particularly challenging in the presence of environmental noise and motion artifacts. In this work, SeismoNet, a highly-sensitive, low-power, multi-node wireless wearable platform is introduced that enables recording of small amplitude acceleration signals (especially SCG signals) and ECG from multiple points on the human body. Using the SeismoNet, we performed a study involving 20 human participants with five nodes placed around the trunk, and showed that combining multiple nodes can improve the estimation performance of heart rate (HR) and respiration rate (RR) by approximately 30% and 23%, respectively. This suggests that combining acceleration data from multiple points on the body can enhance the cardiac and respiratory content of the acquired data, resulting in more accurate predictions. Furthermore, the SeismoNet platform and the dataset collected can be utilized to explore research questions related to multi-point physiological sensing.