Estimation of spinal loading during manual materials handling using inertial motion capture

Frederik Greve Larsen, Frederik Petri Svenningsen, Michael Skipper Andersen, Mark de Zee, Sebastian Skals

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
JournalAnnals of Biomedical Engineering
Volume48
Pages (from-to)805–821
ISSN0090-6964
DOIs
Publication statusPublished - 2020

Keywords

  • Journal Article

Fingerprint

Dive into the research topics of 'Estimation of spinal loading during manual materials handling using inertial motion capture'. Together they form a unique fingerprint.

Cite this