Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
Published in
Knowledge Discovery and Data Mining(2018)External Links:
TL
TL;DR
This paper studies the prediction of heart rate from acceleration using a wrist worn wearable and proposes an online approach to tackle the concept as time passes, which can achieve good predictive performance while using the PPG heart rate sensor infrequently.
Abstract
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).
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