Research Article
Activity Monitoring Using Smart Glasses: Exploring the Feasibility of Pedometry on Head Mounted Displays
@INPROCEEDINGS{10.1007/978-3-030-64991-3_11, author={Zhiquan You and Farnaz Mohammadi and Emily Pascua and Daniel Kale and Abraham Vega and Gian Tolentino and Pedro Angeles and Navid Amini}, title={Activity Monitoring Using Smart Glasses: Exploring the Feasibility of Pedometry on Head Mounted Displays}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health. 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings}, proceedings_a={BODYNETS}, year={2020}, month={12}, keywords={Smart glasses Accelerometer Activity monitoring Salience Peak-to-Peak}, doi={10.1007/978-3-030-64991-3_11} }- Zhiquan You
Farnaz Mohammadi
Emily Pascua
Daniel Kale
Abraham Vega
Gian Tolentino
Pedro Angeles
Navid Amini
Year: 2020
Activity Monitoring Using Smart Glasses: Exploring the Feasibility of Pedometry on Head Mounted Displays
BODYNETS
Springer
DOI: 10.1007/978-3-030-64991-3_11
Abstract
Fitness tracking, fall detection, indoor navigation, and visual aid applications for smart glasses are rapidly emerging. The performance of these applications heavily relies on the accuracy of step detection, which has rarely been studied for smart glasses. In this paper, we develop an accelerometer-based algorithm for step calculation on smart glasses. Designed based on a salience-analysis approach, the algorithm provides a highly accurate step calculation. An activity monitoring application for a commercial Android-based smart glasses (Vuzix M100) is designed and realized for algorithm evaluation. Experimental results from 10 participants wearing the smart glasses running our application achieved average step detection error of 2.6% demonstrating the feasibility of our salience-based algorithm for performing pedometry on smart glasses.
