Edge computing design space exploration for heart rate monitoring.

Edge computing, smart sensors, and health monitoring are boosting current wearable development and enabling the next technological user-centred revolution. Within this context, high added-value applications based on physiological information are gaining attention during the last years. Among the vast physiological metrics available, heart rate variability (HRV) is one of the most used. From such metric, different types of information related to the activity of the autonomic nervous system can be obtained. This fact has led integrated chip manufacturers to foster the design of novel analog front end circuitry for heart rate monitoring, which boosted a wearable smart sensor innovation. Notwithstanding the capabilities and efficiency of these novel sensors, different design space exploration (DSE) procedures must be addressed for every sensor integrated within any wearable system towards maximising the embedded resource usage. On this basis, this paper presents a DSE breakup for every stage involved in a wearable edge device developed by the authors and based on continuous HRV physiological monitoring. The particularities of such a system are detailed and explained. Moreover, time complexity and memory usage comparisons regarding different digital signal processing techniques are provided, which results in a set of potential recommendations for wearable constrained application needs. Finally, a use case is presented based on a rapid stress detection application by using the different DSE recommendations for our specific wearable edge device. This application reaches adequate trade-off precision for detecting physiological HRV activation using only a four-second temporal processing window. • Design Space Exploration is an essential task when facing the development of physiological edge computing devices. • The different morphology of PPG signals directly affects the data processing chain of HR-based wearable devices. • Heart Rate Variability is a reliable metric to detect physiological activation. [ABSTRACT FROM AUTHOR]