  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.
