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Activity Tracking Using Ear-Level Accelerometers
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- Skoglund, Martin, 1981- (författare)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten,Eriksholm Research Centre, Oticon A/S, Denmark
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- Balzi, Giovanni (författare)
- Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Lyngby, Denmark
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- Jensen, Emil Lindegaard (författare)
- Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Lyngby, Denmark
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- (creator_code:org_t)
- 2021-09-17
- 2021
- Engelska.
- Ingår i: Frontiers in digital health. - : Frontiers Media S.A.. - 2673-253X. ; 3
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- accelerometer; activity tracking; classification; hearing aids; hearing healthcare; machine learning; supervised learning
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