Towards Low-Power Heart Rate Estimation Based on User’s Demographics and Activity Level For Wearables

Over the past few years, wearable devices have become quite popular, in particular, smartwatches. One reason for this popularity is the possibility to monitor health and well-being in a non-invasive way. Heart Rate (HR) monitoring is one of the most important health features available in wearables. Normally, HR estimation is achieved using photoplethysmography (PPG), a common low-cost optical technique that achieves fair HR estimation in wearables. However, this technique is energy-consuming and significantly affects the device’s battery life for long-term monitoring – such as during physical exercises. In this work, we proposed a model based on linear regression and a Proportional–Integral–Derivative (PID) controller that uses an accelerometer and user’s demographics to estimate HR. The main goal of this model is to reduce power consumption since the accelerometer is a low-power sensor. We perform experiments to evaluate the performance of our method using three datasets containing more than 180 hours of data composed of a large number of different subjects. The results show that our method is competitive with a PPG-based approach and for some occasions, it is plausible to use such a model in order to save battery.