EMG Validation of a Subject-Specific Thoracolumbar Spine Musculoskeletal Model During Dynamic Activities in Older Adults

Musculoskeletal models can uniquely estimate in vivo demands and injury risk. In this study, we aimed to compare muscle activations from subject-specific thoracolumbar spine OpenSim models with recorded muscle activity from electromyography (EMG) during five dynamic tasks. Specifically, 11 older adu...

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Veröffentlicht in:Annals of biomedical engineering 2023-10, Vol.51 (10), p.2313-2322
Hauptverfasser: Alemi, Mohammad Mehdi, Banks, Jacob J., Lynch, Andrew C., Allaire, Brett T., Bouxsein, Mary L., Anderson, Dennis E.
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Sprache:eng
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Zusammenfassung:Musculoskeletal models can uniquely estimate in vivo demands and injury risk. In this study, we aimed to compare muscle activations from subject-specific thoracolumbar spine OpenSim models with recorded muscle activity from electromyography (EMG) during five dynamic tasks. Specifically, 11 older adults (mean = 65 years, SD = 9) lifted a crate weighted to 10% of their body mass in axial rotation, 2-handed sagittal lift, 1-handed sagittal lift, and lateral bending, and simulated a window opening task. EMG measurements of back and abdominal muscles were directly compared to equivalent model-predicted activity for temporal similarity via maximum absolute normalized cross-correlation (MANCC) coefficients and for magnitude differences via root-mean-square errors (RMSE), across all combinations of participants, dynamic tasks, and muscle groups. We found that across most of the tasks the model reasonably predicted temporal behavior of back extensor muscles (median MANCC = 0.92 ± 0.07) but moderate temporal similarity was observed for abdominal muscles (median MANCC = 0.60 ± 0.20). Activation magnitude was comparable to previous modeling studies, and median RMSE was 0.18 ± 0.08 for back extensor muscles. Overall, these results indicate that our thoracolumbar spine model can be used to estimate subject-specific in vivo muscular activations for these dynamic lifting tasks.
ISSN:0090-6964
1573-9686
1573-9686
DOI:10.1007/s10439-023-03273-3