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Ghezelbash F, Hossein Eskandari A, Robert-Lachaine X, Cao S, Pesteie M, Qiao Z, Shirazi-Adl A, Larivière C. Machine learning applications in spine biomechanics. J Biomech 2024; 166:111967. [PMID: 38388222 DOI: 10.1016/j.jbiomech.2024.111967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/21/2024] [Accepted: 01/28/2024] [Indexed: 02/24/2024]
Abstract
Spine biomechanics is at a transformation with the advent and integration of machine learning and computer vision technologies. These novel techniques facilitate the estimation of 3D body shapes, anthropometrics, and kinematics from as simple as a single-camera image, making them more accessible and practical for a diverse range of applications. This study introduces a framework that merges these methodologies with traditional musculoskeletal modeling, enabling comprehensive analysis of spinal biomechanics during complex activities from a single camera. Additionally, we aim to evaluate their performance and limitations in spine biomechanics applications. The real-world applications explored in this study include assessment in workplace lifting, evaluation of whiplash injuries in car accidents, and biomechanical analysis in professional sports. Our results demonstrate potential and limitations of various algorithms in estimating body shape, kinematics, and conducting in-field biomechanical analyses. In industrial settings, the potential to utilize these new technologies for biomechanical risk assessments offers a pathway for preventive measures against back injuries. In sports activities, the proposed framework provides new opportunities for performance optimization, injury prevention, and rehabilitation. The application in forensic domain further underscores the wide-reaching implications of this technology. While certain limitations were identified, particularly in accuracy of predictions, complex interactions, and external load estimation, this study demonstrates their potential for advancement in spine biomechanics, heralding an optimistic future in both research and practical applications.
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Affiliation(s)
- Farshid Ghezelbash
- Division of Applied Mechanics, Department of Mechanical Engineering, Polytechnique Montréal, Canada.
| | - Amir Hossein Eskandari
- Division of Applied Mechanics, Department of Mechanical Engineering, Polytechnique Montréal, Canada; Institut de Recherche Robert Sauvé en Santé et en Sécurité du Travail, Montréal, Canada
| | | | - Shufan Cao
- Department of Mechanical Engineering and Material Science, Duke University, USA
| | - Mehran Pesteie
- Department of Electrical and Computer Engineering, University of British Columbia, Canada
| | - Zhuohua Qiao
- Department of Mechanical Engineering, McGill University, Canada
| | - Aboulfazl Shirazi-Adl
- Division of Applied Mechanics, Department of Mechanical Engineering, Polytechnique Montréal, Canada
| | - Christian Larivière
- Institut de Recherche Robert Sauvé en Santé et en Sécurité du Travail, Montréal, Canada
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Sibson BE, Banks JJ, Yawar A, Yegian AK, Anderson DE, Lieberman DE. Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running. Sci Rep 2024; 14:234. [PMID: 38168540 PMCID: PMC10762015 DOI: 10.1038/s41598-023-50652-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
Optical motion capture (OMC) is considered the best available method for measuring spine kinematics, yet inertial measurement units (IMU) have the potential to collect data outside the laboratory. When combined with musculoskeletal modeling, IMU technology may be used to estimate spinal loads in real-world settings. To date, IMUs have not been validated for estimates of spinal movement and loading during both walking and running. Using OpenSim Thoracolumbar Spine and Ribcage models, we compare IMU and OMC estimates of lumbosacral (L5/S1) and thoracolumbar (T12/L1) joint angles, moments, and reaction forces during gait across six speeds for five participants. For comparisons, time series are ensemble averaged over strides. Comparisons between IMU and OMC ensemble averages have low normalized root mean squared errors (< 0.3 for 81% of comparisons) and high, positive cross-correlations (> 0.5 for 91% of comparisons), suggesting signals are similar in magnitude and trend. As expected, joint moments and reaction forces are higher during running than walking for IMU and OMC. Relative to OMC, IMU overestimates joint moments and underestimates joint reaction forces by 20.9% and 15.7%, respectively. The results suggest using a combination of IMU technology and musculoskeletal modeling is a valid means for estimating spinal movement and loading.
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Affiliation(s)
- Benjamin E Sibson
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.
| | - Jacob J Banks
- Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Orthopedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Ali Yawar
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Andrew K Yegian
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Dennis E Anderson
- Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Orthopedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Daniel E Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
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Salisu S, Ruhaiyem NIR, Eisa TAE, Nasser M, Saeed F, Younis HA. Motion Capture Technologies for Ergonomics: A Systematic Literature Review. Diagnostics (Basel) 2023; 13:2593. [PMID: 37568956 PMCID: PMC10416907 DOI: 10.3390/diagnostics13152593] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023] Open
Abstract
Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.
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Affiliation(s)
- Sani Salisu
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
- Department of Information Technology, Federal University Dutse, Dutse 720101, Nigeria
| | | | | | - Maged Nasser
- Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia;
| | - Faisal Saeed
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK;
| | - Hussain A. Younis
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
- College of Education for Women, University of Basrah, Basrah 61004, Iraq
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Lind CM, Abtahi F, Forsman M. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094259. [PMID: 37177463 PMCID: PMC10181376 DOI: 10.3390/s23094259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/14/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.
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Affiliation(s)
- Carl Mikael Lind
- IMM Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Farhad Abtahi
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 141 57 Huddinge, Sweden
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, 141 86 Huddinge, Sweden
| | - Mikael Forsman
- IMM Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 141 57 Huddinge, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, 113 65 Stockholm, Sweden
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