A comparison of static and dynamic optimization muscle force predictions during wheelchair propulsion
Year: 2014
Type: article
Abstract: The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4% Fmax error in the middle deltoid) to good (6.4% Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction.
Source: Journal of Biomechanics
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
Funders: U.S. Department of Defense, U.S. Department of Veterans Affairs, Cancer Research Institute, Breast Cancer Research Foundation, Bristol-Myers Squibb, Eli Lilly and Company, AstraZeneca, Gilead Sciences, Susan G. Komen, Irish Research eLibrary, Cancer Research UK, European Commission, National Breast Cancer Foundation, Higher Education Authority, Irish Cancer Society, Science Foundation Ireland, Agence Nationale de la Recherche, Ministerie van Volksgezondheid, Welzijn en Sport, Svenska Sällskapet för Medicinsk Forskning, KU Leuven, Fondation ARC pour la Recherche sur le Cancer, Ligue Contre le Cancer, Peter MacCallum Cancer Centre, KWF Kankerbestrijding, National Institutes of Health, Medical Research Council, Engineering and Physical Sciences Research Council, National Health and Medical Research Council, Japan Society for the Promotion of Science, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, Eunice Kennedy Shriver National Institute of Child Health and Human Development, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Awards: SYSTEMS BIOLOGY APPROACH TO PREDICTING AND ASSESSING RESPONSE TO CHEMORADIATION FOR BRAIN TUMORS, G1159005989, G1307000385, G1313646905, G1357738703, G1430642689, Tissue resident memory T cells- delineating their role in breast cancer anti-tumor immunity, G1828163000, G1998022053, G2132432765, G2291306530, CLoud ARtificial Intelligence For pathologY, Mayo Rehabilitation Research Training Center, G2435305877, G2489430190, G2689512122, G3010527862, Precision Oncology Ireland, G3068466812, G3182923297, G3203138580, G3429535173, Methods and Tools for Integrating Pathomics Data into Cancer Registries, G3629664845, G3679001064, G3716872827, Optimal Management of Gender-Specific Cancers via Efficient Use of Protein Profiling, Digital Pathology and Systems Medicine Tools (OPTi-PREDICT), CHIRP Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung Cancers, G406353596, G4119534169, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, G42316314, G424082930, Biomechanics of Wheelchair Propulsion, G431899019, G434337789, フランス労務審判所(Conseil de Prud'hommes)の研究, G4801435656, G4876897937, G4904196628, G4950536974, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, MR Fingerprinting and Computerized Decision Support for Prostate Cancer, G5470642588, G5591417150, Computerized histologic image predictor of cancer outcome, PaRis Artificial Intelligence Research InstitutE, Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal Carcinoma, G5850682332, Possible roles of human mast cells in the formation of xanthelasma palpebrarum, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, G606701110, Characterizing and predicting colitis in immune checkpoint blockade-treated cancer patients, G620757052, G6254892548, PRIMAVO: Interactive exploration of cancer patient precision immune monitoring data in clinical trials, G6456321151, G6803667113, Translating molecular pathology into cancer diagnostics, G6973860336, G7023770567, G7130193, Causal Modelling with Graph Neural Networks for Personalised Medicine in Computational Pathology, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, G7502883741, Institut Q-LIFE, Illuminating the evolutionary history of colorectal cancer metastasis: basic principles and clinical applications, MSK SPORE in Genomic Instability in Breast Cancer, G7935030457, G8166743225, G8173526563, G8456267142, G8600969490, G8784895187, COMPUTATIONAL HISTOMORPHOMETRIC PREDICTOR OF PATHOLOGIC RESPONSE AND DISEASE RECURRENCE FOR TRIPLE-NEGATIVE BREAST CANCER, G9000025035, G9000056613, G9000227104, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC), High-Dimensional Immune Monitoring of NCI-Supported Immunotherapy Trials, Mayo Clinic Breast Cancer SPORE, An AI-enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit, G936396391, G937623596
OpenAlex ID: W2171750522