Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain
Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current prior...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2022.868684/full |
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| author | Anastasia V. Keller Anastasia V. Keller Abel Torres-Espin Thomas A. Peterson Jacqueline Booker Conor O’Neill Jeffrey C Lotz Jeannie F Bailey Adam R. Ferguson Adam R. Ferguson Robert P. Matthew |
| author_facet | Anastasia V. Keller Anastasia V. Keller Abel Torres-Espin Thomas A. Peterson Jacqueline Booker Conor O’Neill Jeffrey C Lotz Jeannie F Bailey Adam R. Ferguson Adam R. Ferguson Robert P. Matthew |
| author_sort | Anastasia V. Keller |
| collection | DOAJ |
| description | Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology. |
| first_indexed | 2024-04-13T17:07:55Z |
| format | Article |
| id | doaj.art-6c0ff3132589493b835a5e400b50a73d |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-13T17:07:55Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-6c0ff3132589493b835a5e400b50a73d2022-12-22T02:38:24ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852022-04-011010.3389/fbioe.2022.868684868684Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back PainAnastasia V. Keller0Anastasia V. Keller1Abel Torres-Espin2Thomas A. Peterson3Jacqueline Booker4Conor O’Neill5Jeffrey C Lotz6Jeannie F Bailey7Adam R. Ferguson8Adam R. Ferguson9Robert P. Matthew10Brain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United StatesSan Francisco Veterans Affairs Healthcare System, San Francisco, CA, United StatesBrain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United StatesBrain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United StatesSan Francisco Veterans Affairs Healthcare System, San Francisco, CA, United StatesDepartment of Physical Therapy and Rehabilitation Science, University of California, San Francisco, San Francisco, CA, United StatesChronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.https://www.frontiersin.org/articles/10.3389/fbioe.2022.868684/fullnonlinear principal component analysisbiomechanicschronic low back painsit-to-standmovement strategies |
| spellingShingle | Anastasia V. Keller Anastasia V. Keller Abel Torres-Espin Thomas A. Peterson Jacqueline Booker Conor O’Neill Jeffrey C Lotz Jeannie F Bailey Adam R. Ferguson Adam R. Ferguson Robert P. Matthew Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain Frontiers in Bioengineering and Biotechnology nonlinear principal component analysis biomechanics chronic low back pain sit-to-stand movement strategies |
| title | Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain |
| title_full | Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain |
| title_fullStr | Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain |
| title_full_unstemmed | Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain |
| title_short | Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain |
| title_sort | unsupervised machine learning on motion capture data uncovers movement strategies in low back pain |
| topic | nonlinear principal component analysis biomechanics chronic low back pain sit-to-stand movement strategies |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2022.868684/full |
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