Musculoskeletal Modeling of Back-Support Exoskeletons: Comparative Evaluation of Spine Loads, Muscle Activity, and Optimization Strategies

Work-related musculoskeletal disorders, particularly work-related low back pain, account for a substantial proportion of lost workdays in the United States, with manual lifting identified as a primary occupational risk factor. Back-support exoskeletons (BSEs) have emerged as a promising ergonomic intervention to reduce spinal loading during lifting tasks. While prior studies indicate reductions in trunk muscle activity with BSE use, experimental evaluations were often limited by constraints on measuring muscle activity via surface electromyography. Optimization-based musculoskeletal models provide an alternative means of estimating muscle activity and intervertebral joint forces (IJFs), but inherent model assumptions require systematic evaluation. The objective of this dissertation was to assess model-based estimates of spine loading and muscle activity across modeling tools and optimization strategies during lifting tasks performed with and without BSEs. The first study compared IJF estimates from OpenSim and the AnyBody Modeling System during symmetric and asymmetric lifting tasks that were performed with and without two BSEs. Both models estimated reduced spinal loading with BSE use, though OpenSim generally predicted larger reductions. Agreement between models was strong for axial compression but weak for shear forces, particularly during asymmetric tasks. The second study compared model-based trunk extensor muscle activity estimates to normalized surface electromyography data. Models captured overall reductions in peak muscle activity with BSE use, but agreement with experimental data was reduced in BSE conditions. The third study examined the influence of different optimization criteria within the AnyBody Modeling System on muscle activity and IJF estimates. Quadratic and cubic criteria generate estimates in better agreement with electromyography than a Min/Max criterion, and estimated IJF magnitudes varied across criteria. Despite differences in IJF estimates, all criteria indicated similar relative reductions in IJFs with BSE use. Collectively, these findings highlight key sources of variability in the predictions generated using musculoskeletal models. The results can help inform best practices for evaluating the biomechanical effects of back-support exoskeletons for lifting tasks.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2026

Author dc:creator

• Behjati Ashtiani, Mohamad

Chair dc:contributor.committeechair

• Nussbaum, Maury A.

Committee members dc:contributor.committeemember

• Madigan, Michael L.
• Kim, Sun Wook
• Lim, Sol Ie

• Musculoskeletal Modeling
• Occupational Exoskeletons
• Low-Back Pain
• Electromyography
• Occupational Biomechanics
• Motion Analysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:45729

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/141279