Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
This work addresses energy efficiency for wearable devices in healthcare monitoring, but it is incremental as it builds on existing methods for heart rate prediction.
The paper tackles the problem of predicting heart rate from acceleration data using a wrist-worn wearable to save energy compared to photoplethysmography (PPG) sensors, achieving a Mean Absolute Error of 2.89 while using the PPG sensor only 20.25% of the time.
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).