  Health metrics from wrist-worn devices demand an automatic dominant hand
prediction to keep an accurate operation. The prediction would improve
reliability, enhance the consumer experience, and encourage further development
of healthcare applications. This paper aims to evaluate the use of
physiological and spatiotemporal context information from a two-hand experiment
to predict the wrist placement of a commercial smartwatch. The main
contribution is a methodology to obtain an effective model and features from
low sample rate physiological sensors and a self-reported context survey.
Results show an effective dominant hand prediction using data from a single
subject under real-life conditions.
