  Wearable medical technology has become increasingly popular in recent years.
One function of wearable health devices is stress detection, which relies on
sensor inputs to determine the mental state of patients. This continuous,
real-time monitoring can provide healthcare professionals with vital
physiological data and enhance the quality of patient care. Current methods of
stress detection lack: (i) robustness -- wearable health sensors contain high
levels of measurement noise that degrades performance, and (ii) adaptation --
static architectures fail to adapt to changing contexts in sensing conditions.
We propose to address these deficiencies with SELF-CARE, a generalized
selective sensor fusion method of stress detection that employs novel
techniques of context identification and ensemble machine learning. SELF-CARE
uses a learning-based classifier to process sensor features and model the
environmental variations in sensing conditions known as the noise context.
SELF-CARE uses noise context to selectively fuse different sensor combinations
across an ensemble of models to perform robust stress classification. Our
findings suggest that for wrist-worn devices, sensors that measure motion are
most suitable to understand noise context, while for chest-worn devices, the
most suitable sensors are those that detect muscle contraction. SELF-CARE
demonstrates state-of-the-art performance on the WESAD dataset. Using
wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the
3-class and 2-class stress classification problems, respectively. For
chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68%
(2-class) classification accuracy. This work demonstrates the benefits of
utilizing selective, context-aware sensor fusion in mobile health sensing that
can be applied broadly to Internet of Things applications.
