# Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning

## TL;DR

This paper explores how machine learning, especially neural networks, can reduce measurement errors in heart rate variability data collected from consumer smartwatches by accounting for wearer movement using additional sensor data.

## Contribution

It introduces a method to minimize HRV measurement errors from smartwatches by incorporating accelerometer data through neural learning models, addressing movement-related inaccuracies.

## Key findings

- Significant correlation between movement and HRV measurement error.
- Using accelerometer data reduces measurement error.
- Neural learning can effectively improve smartwatch HRV accuracy.

## Abstract

The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements.