Validation of markerless vision-based motion capture for ergonomics risk assessment

Work-related musculoskeletal disorders impact millions annually, often due to awkward postures and heavy lifting. Vision-based markerless optical motion capture (ML-OMC) systems have gained attention as a possible solution for identifying ergonomic risks in workplace settings. However, their reliability remains unknown compared to marker-based optical motion capture (MB-OMC) and inertial measurement units (IMUs). This study reports on a comparative analysis of an ML-OMC against MB-OMC and IMUs and its suitability for joint reaction force estimation. Eight participants performed lifting, a task considered physically demanding among manual handling activities, while their joint angles were recorded using the three measurement systems, and joint reaction forces were determined using joint angle data and ground reaction forces through biomechanical modeling. Furthermore, postural ergonomic assessment scores were computed for the lifting initiation posture of the activity using data from the three systems and biomechanics experts’ inputs. The back angle obtained by ML-OMC exhibited a strong correlation (0.95) with both MB-OMC and IMUs, along with small RMSE values of 6.5° and 9.9° compared to the readouts from MB-OMC and IMUs, respectively. The L5-S1 joint reaction forces obtained by ML-OMC showed a high correlation (0.91 with MB-OMC and 0.85 with IMUs), and small RMSE and normalized RMSE values. Additionally, postural ergonomic assessment scores obtained from ML-OMC aligned with MB-OMC for 87 % of participants, showing significant consistency compared to the notable variation seen with expert-derived scores. These findings underscore the potential of ML-OMC as a dependable in-field ergonomic risk assessment tool for preventing work-related musculoskeletal disorders. • The markerless system was evaluated against established methods for estimating joint angles, forces, and ergonomic risks. • The markerless system showed 0.95 correlation with marker-based systems and IMUs for back angle, with minimal RMSE. • The markerless system outcomes estimated L5-S1 forces with close correlation with marker-based system (0.91) and IMUs (0.85). • The markerless system aligned with the marker-based system in postural ergonomic assessments for 87% of participants. • The findings highlight the markerless system's potential for in-field ergonomics assessments to prevent work injuries.

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Publication Details

Journal
International Journal of Industrial Ergonomics
Published
2025-04-06
DOI
https://doi.org/10.1016/j.ergon.2025.103734
Citations
9
Primary Topic
Ergonomics and Musculoskeletal Disorders
Type
article
Field-Weighted Citation Impact
21.95

Funders

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article

Validation of markerless vision-based motion capture for ergonomics risk assessment

Hossein Rouhani, Mahdi Tavakoli, Alireza Golabchi, Linda Miller et al.
9 citations
International Journal of Industrial Ergonomics
Ergonomics and Musculoskeletal Disorders
21.95
article

Validation of markerless vision-based motion capture for ergonomics risk assessment

Hossein Rouhani, Mahdi Tavakoli, Alireza Golabchi, Linda Miller, Armin Bonakdar, Negar Riahi, Maryam Shakourisalim
article en
9 citations

Abstract

Work-related musculoskeletal disorders impact millions annually, often due to awkward postures and heavy lifting. Vision-based markerless optical motion capture (ML-OMC) systems have gained attention as a possible solution for identifying ergonomic risks in workplace settings. However, their reliability remains unknown compared to marker-based optical motion capture (MB-OMC) and inertial measurement units (IMUs). This study reports on a comparative analysis of an ML-OMC against MB-OMC and IMUs and its suitability for joint reaction force estimation. Eight participants performed lifting, a task considered physically demanding among manual handling activities, while their joint angles were recorded using the three measurement systems, and joint reaction forces were determined using joint angle data and ground reaction forces through biomechanical modeling. Furthermore, postural ergonomic assessment scores were computed for the lifting initiation posture of the activity using data from the three systems and biomechanics experts’ inputs. The back angle obtained by ML-OMC exhibited a strong correlation (0.95) with both MB-OMC and IMUs, along with small RMSE values of 6.5° and 9.9° compared to the readouts from MB-OMC and IMUs, respectively. The L5-S1 joint reaction forces obtained by ML-OMC showed a high correlation (0.91 with MB-OMC and 0.85 with IMUs), and small RMSE and normalized RMSE values. Additionally, postural ergonomic assessment scores obtained from ML-OMC aligned with MB-OMC for 87 % of participants, showing significant consistency compared to the notable variation seen with expert-derived scores. These findings underscore the potential of ML-OMC as a dependable in-field ergonomic risk assessment tool for preventing work-related musculoskeletal disorders. • The markerless system was evaluated against established methods for estimating joint angles, forces, and ergonomic risks. • The markerless system showed 0.95 correlation with marker-based systems and IMUs for back angle, with minimal RMSE. • The markerless system outcomes estimated L5-S1 forces with close correlation with marker-based system (0.91) and IMUs (0.85). • The markerless system aligned with the marker-based system in postural ergonomic assessments for 87% of participants. • The findings highlight the markerless system's potential for in-field ergonomics assessments to prevent work injuries.

International Journal of Industrial ErgonomicsVol. 107
Public Works and Government Services Canada (CA), University of Alberta (CA), Glenrose Rehabilitation Hospital (CA)
Alberta Innovates, Natural Sciences and Engineering Research Council of Canada
Climate action
Openalex Percentile: Top 1%
Ergonomics and Musculoskeletal Disorders
21.95
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