Effects of external hand force modeling on validity of inverse analysis of lifting.

Eunsik Choi, Ilseung Park, Jeongin Moon, Jangwhan Ahn, Jooeun Ahn

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Eunsik ChoiDepartment of Physical Education, Seoul National University, Seoul, Republic of Korea.

Ilseung ParkDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

Jeongin MoonDepartment of Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, MA, USA.

Jangwhan AhnDepartment of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Jooeun AhnDepartment of Physical Education, Seoul National University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manual lifting tasks pose the risk of musculoskeletal injury, including damage to lumbar intervertebral discs. To assess the risk, spinal joint reaction forces are often estimated using musculoskeletal simulations. However, such analyses require information of external hand force and moment (EHF&M), which has mostly been estimated using simplified models without sufficient validation. This study evaluates the validity of two common EHF&M models by comparing their analysis outcomes to those resulting from directly measured EHF&M. Simplified models yielded negligible errors during the middle phase of symmetric lifting and lowering but caused substantial errors at the beginning and end of each task, leading to inaccurate estimation of peak spinal joint reaction forces. These findings highlight the limitations of simplified models in evaluating mechanical factors of injury risk. The measurement platform and analysis method developed in this study can also contribute to validating EHF&M modeling for other tasks and improving assessment accuracy.

Indexed as

Biological sciencesBiomechanics

Identifiers

PMID41541681

DOI10.1016/j.isci.2025.114146

PMCPMC12800434

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.