Predicting dominant hand from spatiotemporal context varying physiological data

TL;DR

This study develops a methodology to predict the dominant hand using physiological and spatiotemporal data from wrist-worn devices, improving health metric accuracy and user experience in real-life conditions.

Contribution

It introduces a novel approach combining low sample rate physiological sensors and self-reported context to accurately predict dominant hand in real-world scenarios.

Findings

Effective dominant hand prediction achieved with data from a single subject.

Methodology demonstrates potential for real-life health monitoring applications.

Utilizes low sample rate sensors and self-reported data for accurate predictions.

Abstract

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.