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.
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Funding Information: This work was supported by the UK EPSRC by Grant nos. EP/I033602/1 and EP/K019759/1. This research was partly supported by the project of National Key R&D Program of China (No.2018YFC2001300), the project of National Natural Science Foundation of China (No.91948302, No.91848204, No.52005209, and No.51675222). Funding Information: This work was supported by the UK EPSRC by Grant nos. EP/I033602/1 and EP/K019759/1. Publisher Copyright: © 2022, The Author(s).
- Inverse dynamics
- Predictive models
ASJC Scopus subject areas
- Modelling and Simulation
- Mechanical Engineering