Data Quality Assessment for the Validation of Synchronization Performance in an Innovative Wireless Multi-Node Monitoring Platform
Alessio Serrani, Andréa Aliverti · 2023
Nowadays the health-monitoring scenario increasingly demands acquisition platforms able to collect at the same time multiple bio-signals. The main engineering challenge is to make these multi-nodes platforms fully wireless and wearable, allowing their use during daily life activities. In order to keep sensing nodes synchronized with each other, these systems need structured wireless architectures (i.e. synchronization engines). In this paper we propose a quantitative analysis to evaluate and to improve the wireless synchronization engine of an innovative wearable multi-node monitoring platform. We exploited the data collector as time-logger and we evaluated data about synchronization delay during the tuning of different architecture parameters. In particular, we performed several data collections evaluating a range of different frequencies used to exchange synchronization packets and testing the introduction of a dynamic adaption in the Connection Interval (a specific Bluetooth Low Energy (BLE) parameter). We achieved the overall best performance (1,18 16-MHz-ticks as synchronization delay median value, with 1,60 16-MHz-ticks as Min-Max range) adopting both 40 Hz as synchronization frequency and the Connection Interval dynamic adaption. Our final goal is to investigate the most performing settings in order to increase the quality of physiological data collected with the platform under analysis, considering the lower the synchronization delay, the higher the data quality.