Idioma:

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Imani Nejad, Zohreh ; Khalili, Khalil ; Hosseini Nasab, Seyyed Hamed ; Schütz, Pascal ; Damm, Philipp ; Trepczynski, Adam ; Taylor, William R. ; Smith, Colin R.

Annals of biomedical engineering, 2020-04, Vol.48 (4), p.1430-1440 [Periódico revisado por pares]

Cham: Springer International Publishing

Texto completo disponível

Citações Citado por

Enviar para

Título:
The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets
Autor: Imani Nejad, Zohreh ; Khalili, Khalil ; Hosseini Nasab, Seyyed Hamed ; Schütz, Pascal ; Damm, Philipp ; Trepczynski, Adam ; Taylor, William R. ; Smith, Colin R.
Assuntos: Activation ; Aged ; Biochemistry ; Biological and Medical Physics ; Biomechanical Phenomena ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Classical Mechanics ; Computer Simulation ; Contact force ; Contraction ; Datasets ; Electromyography ; Etiology ; Female ; Gait ; Hip ; Humans ; Knee ; Knee Joint - physiology ; Male ; Middle Aged ; Models, Biological ; Movement - physiology ; Muscle contraction ; Muscle, Skeletal - physiology ; Muscles ; Musculoskeletal system ; Optimization ; Original ; Original Article ; Parameter uncertainty ; Walking
É parte de: Annals of biomedical engineering, 2020-04, Vol.48 (4), p.1430-1440
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Associate Editor Thurmon E. Lockhart oversaw the review of this article.
Descrição: Musculoskeletal models enable non-invasive estimation of knee contact forces (KCFs) during functional movements. However, the redundant nature of the musculoskeletal system and uncertainty in model parameters necessitates that model predictions are critically evaluated. This study compared KCF and muscle activation patterns predicted using a scaled generic model and OpenSim static optimization tool against in vivo measurements from six patients in the CAMS-knee datasets during level walking and squatting. Generally, the total KCFs were under-predicted (RMS: 47.55%BW, R 2 : 0.92) throughout the gait cycle, but substiantially over-predicted (RMS: 105.7%BW, R 2 : 0.81) during squatting. To understand the underlying etiology of the errors, muscle activations were compared to electromyography (EMG) signals, and showed good agreement during level walking. For squatting, however, the muscle activations showed large descrepancies especially for the biceps femoris long head. Errors in the predicted KCF and muscle activation patterns were greatest during deep squat. Hence suggesting that the errors mainly originate from muscle represented at the hip and an associated muscle co-contraction at the knee. Furthermore, there were substaintial differences in the ranking of subjects and activities based on peak KCFs in the simulations versus measurements. Thus, future simulation study designs must account for subject-specific uncertainties in musculoskeletal predictions.
Editor: Cham: Springer International Publishing
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Imani Nejad, Zohreh ; Khalili, Khalil ; Hosseini Nasab, Seyyed Hamed ; Schütz, Pascal ; Damm, Philipp ; Trepczynski, Adam ; Taylor, William R. ; Smith, Colin R.

Cham: Springer International Publishing

Buscando em bases de dados remotas. Favor aguardar.