Predicting dominant hand from spatiotemporal context varying physiological data
This addresses a practical issue for users of commercial smartwatches and healthcare applications, but it is incremental as it builds on existing methods with new data.
The paper tackled the problem of automatically predicting the dominant hand from wrist-worn device data to improve reliability and user experience, achieving effective prediction using data from a single subject under real-life conditions.
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.