A three-dimensional whole-body model to predict human walking on level ground
Biomechanics and Modeling in Mechanobiology, ISSN: 1617-7940, Vol: 21, Issue: 6, Page: 1919-1933
2022
- 2Citations
- 20Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Predictive simulation of human walking has great potential in clinical motion analysis and rehabilitation engineering assessment, but large computational cost and reliance on measurement data to provide initial guess have limited its wide use. We developed a computationally efficient model combining optimization and inverse dynamics to predict three-dimensional whole-body motions and forces during human walking without relying on measurement data. Using the model, we explored two different optimization objectives, mechanical energy expenditure and the time integral of normalized joint torque. Of the two criteria, the sum of the time integrals of the normalized joint torques produced a more realistic walking gait. The reason for this difference is that most of the mechanical energy expenditure is in the sagittal plane (based on measurement data) and this leads to difficulty in prediction in the other two planes. We conclude that mechanical energy may only account for part of the complex performance criteria driving human walking in three dimensions.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85140602147&origin=inward; http://dx.doi.org/10.1007/s10237-022-01629-7; http://www.ncbi.nlm.nih.gov/pubmed/36287314; https://link.springer.com/10.1007/s10237-022-01629-7; https://dx.doi.org/10.1007/s10237-022-01629-7; https://link.springer.com/article/10.1007/s10237-022-01629-7
Springer Science and Business Media LLC
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