Automatically evaluating balance using machine learning and data from a single inertial measurement unit
Abstract Background Recently, 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 me...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-021-00894-4 |
| _version_ | 1818886235251277824 |
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| author | Fahad Kamran Kathryn Harrold Jonathan Zwier Wendy Carender Tian Bao Kathleen H. Sienko Jenna Wiens |
| author_facet | Fahad Kamran Kathryn Harrold Jonathan Zwier Wendy Carender Tian Bao Kathleen H. Sienko Jenna Wiens |
| author_sort | Fahad Kamran |
| collection | DOAJ |
| description | Abstract Background Recently, 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. Findings Ten 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). Conclusions Unprocessed 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. |
| first_indexed | 2024-12-19T16:18:06Z |
| format | Article |
| id | doaj.art-f22daaae6fa342f9aa2e7ae2b33ec155 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-19T16:18:06Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-f22daaae6fa342f9aa2e7ae2b33ec1552022-12-21T20:14:34ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032021-07-011811710.1186/s12984-021-00894-4Automatically evaluating balance using machine learning and data from a single inertial measurement unitFahad Kamran0Kathryn Harrold1Jonathan Zwier2Wendy Carender3Tian Bao4Kathleen H. Sienko5Jenna Wiens6Computer Science and Engineering, University of MichiganMechanical Engineering, University of MichiganMechanical Engineering, University of MichiganDepartment of Otolaryngology, Michigan MedicineMechanical Engineering, University of MichiganMechanical Engineering, University of MichiganComputer Science and Engineering, University of MichiganAbstract Background Recently, 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. Findings Ten 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). Conclusions Unprocessed 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.https://doi.org/10.1186/s12984-021-00894-4Balance trainingWearable sensorsMachine learningTelerehabilitation |
| spellingShingle | Fahad Kamran Kathryn Harrold Jonathan Zwier Wendy Carender Tian Bao Kathleen H. Sienko Jenna Wiens Automatically evaluating balance using machine learning and data from a single inertial measurement unit Journal of NeuroEngineering and Rehabilitation Balance training Wearable sensors Machine learning Telerehabilitation |
| title | Automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| title_full | Automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| title_fullStr | Automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| title_full_unstemmed | Automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| title_short | Automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| title_sort | automatically evaluating balance using machine learning and data from a single inertial measurement unit |
| topic | Balance training Wearable sensors Machine learning Telerehabilitation |
| url | https://doi.org/10.1186/s12984-021-00894-4 |
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