Fewer Samples for a Longer Life Span: Towards Long-Term Wearable PPG Analysis

Abstract

Photoplethysmography (PPG) sensors have become a prevalent feature included in current wearables, as the cost and size of current PPG modules have dropped significantly. Research in the analysis of PPG data has recently expanded beyond the fast and accurate characterization of heart rate, into the adaptive handling of artifacts within the signal and even the capturing of respiration rate. In this paper, we instead explore using state-of-the-art PPG sensor modules for long-term wearable deployment and the observation of trends over minutes, rather than seconds. By focusing specifically on lowering the sampling rate and via analysis of the spectrum of frequencies alone, our approach minimizes the costly illumination-based sensing and can be used to detect the dominant frequencies of heart rate and respiration rate, but also enables to infer on activity of the sympathetic nervous system. We show in two experiments that such detections and measurements can still be achieved at low sampling rates down to 10 Hz, within a power-efficient platform. This approach enables miniature sensor designs that monitor average heart rate, respiration rate, and sympathetic nerve activity over longer stretches of time.