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Automatically evaluating balance using machine learning and data from a single inertial measurement unit

Authors
  • Kamran, Fahad1
  • Harrold, Kathryn1
  • Zwier, Jonathan1
  • Carender, Wendy2
  • Bao, Tian1
  • Sienko, Kathleen H.1
  • Wiens, Jenna1
  • 1 University of Michigan, Ann Arbor, USA , Ann Arbor (United States)
  • 2 Michigan Medicine, Ann Arbor, USA , Ann Arbor (United States)
Type
Published Article
Journal
Journal of NeuroEngineering and Rehabilitation
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jul 13, 2021
Volume
18
Issue
1
Identifiers
DOI: 10.1186/s12984-021-00894-4
Source
Springer Nature
Keywords
Disciplines
  • Short Report
License
Green

Abstract

BackgroundRecently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment.FindingsTen participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665).ConclusionsUnprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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