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Related Experiment Video

Updated: Oct 15, 2025

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Activity Tracking Using Ear-Level Accelerometers.

Martin A Skoglund1,2, Giovanni Balzi3, Emil Lindegaard Jensen3

  • 1Division of Automatic Control, Department of Electrical Engineering, The Institute of Technology, Linköping University, Linkoping, Sweden.

Frontiers in Digital Health
|October 29, 2021
PubMed
Summary
This summary is machine-generated.

Ear-level accelerometers in hearing aids can accurately track user activities, matching waist-mounted device performance. This advancement supports new hearing healthcare applications by enabling personalized device adjustments based on motion and activity.

Keywords:
accelerometeractivity trackingclassificationhearing aidshearing healthcaremachine learningsupervised learning

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Area of Science:

  • Biomedical Engineering
  • Sensor Technology
  • Machine Learning

Background:

  • Modern hearing aids (HAs) integrate sensor technology for personalization and self-adaptation.
  • Current HAs use accelerometers for motion activity classification, but ear-level applications are under-explored.

Purpose of the Study:

  • To investigate the feasibility of activity tracking using ear-level accelerometers.
  • To compare the accuracy of ear-level accelerometers against traditional waist-mounted sensors.

Main Methods:

  • Supervised learning algorithms were employed for activity classification.
  • 21 subjects performed nine distinct activities wearing ear-level and waist-level accelerometers.

Main Results:

  • The combination of Bagging and Classification trees yielded the highest accuracy.
  • Ear-level sensors achieved 84% overall accuracy, comparable to waist-level (90%) and combined (91%).
  • Activities like standing, jogging, and walking showed over 90% accuracy; step-detection reached 95%.

Conclusions:

  • Ear-level accelerometers demonstrate comparable accuracy to waist-mounted sensors for activity classification.
  • Step-detection accuracy from ear-level sensors rivals high-performance wrist devices.
  • Findings support the development of activity-aware applications in hearing healthcare.