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Estimation of Spinal Loading During Manual Materials Handling Using Inertial Motion Capture


Musculoskeletal models have traditionally relied on measurements of segment kinematics and ground reaction forces and moments (GRF&Ms) from marked-based motion capture and floor-mounted force plates, which are typically limited to laboratory settings. Recent advances in inertial motion capture (IMC) as well as methods for predicting GRF&Ms have enabled the acquisition of these input data in the field. Therefore, this study evaluated the concurrent validity of a novel methodology for estimating the dynamic loading of the lumbar spine during manual materials handling based on a musculoskeletal model driven exclusively using IMC data and predicted GRF&Ms. Trunk kinematics, GRF&Ms, L4–L5 joint reaction forces (JRFs) and erector spinae muscle forces from 13 subjects performing various lifting and transferring tasks were compared to a model driven by simultaneously recorded skin-marker trajectories and force plate data. Moderate to excellent correlations and relatively low magnitude differences were found for the L4–L5 axial compression, erector spinae muscle and vertical ground reaction forces during symmetrical and asymmetrical lifting, but discrepancies were also identified between the models, particularly for the trunk kinematics and L4–L5 shear forces. Based on these results, the presented methodology can be applied for estimating the relative L4–L5 axial compression forces under dynamic conditions during manual materials handling in the field.

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Ground reaction forces and moments


Inertial motion capture


Joint reaction force


Inverse dynamic analysis


Optical motion capture


Ground reaction force


Inertial measurement unit


Optical motion capture with measured ground reaction forces


Optical motion capture with predicted ground reaction forces


Inertial motion capture with predicted ground reaction forces


Ground reaction moment


Percentage of body weight


Percentage of body weight times body height


Root-mean-square error


Relative root-mean-square error


Intraclass correlation coefficient


Limits of agreement


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This work was supported by the Independent Research Fund Denmark under Grant No. DFF-7026-00099 to Sebastian Skals.

Conflict of interest

Mark de Zee is co-founder of the company AnyBody Technology A/S that owns and sells the AnyBody Modeling System, which was used for the simulations. Mark de Zee is also a minority shareholder in the company.

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Correspondence to Sebastian Skals.

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Larsen, F.G., Svenningsen, F.P., Andersen, M.S. et al. Estimation of Spinal Loading During Manual Materials Handling Using Inertial Motion Capture. Ann Biomed Eng 48, 805–821 (2020).

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  • Musculoskeletal modelling
  • Inertial motion capture
  • Inverse dynamic analysis
  • Predicted ground reaction forces and moments
  • Manual materials handling
  • Low back loading