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Health metrics from wrist-worn devices demand an automatic dominant handprediction to keep an accurate operation. The prediction would improvereliability, enhance the consumer experience, and encourage further developmentof healthcare applications. This paper aims to evaluate the use ofphysiological and spatiotemporal context information from a two-hand experimentto predict the wrist placement of a commercial smartwatch. The maincontribution is a methodology to obtain an effective model and features fromlow sample rate physiological sensors and a self-reported context survey.Results show an effective dominant hand prediction using data from a singlesubject under real-life conditions.

Predicting dominant hand from spatiotemporal context varying physiological data