Activity Tracking Using Ear-Level Accelerometers

Frontiers in Digital Health

Emil Lindegaard Jensen¹  Giovanni Balzi¹  Martin A. Skoglund³  Sergi Rotger-Griful³  Tanveer A. Bhuiyan⁴ 

[1] Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Lyngby, Denmark;Division of Automatic Control, Department of Electrical Engineering, The Institute of Technology, Linköping University, Linkoping, Sweden;Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark;Oticon A/S, Smorum, Denmark;

关键词: activity tracking;    accelerometer;    classification;    machine learning;    supervised learning;    hearing aids;   

DOI  :  10.3389/fdgth.2021.724714

来源: DOAJ

【 摘 要 】

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research.Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location.Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities.Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging.Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.

【 授权许可】

Unknown