Comparison of human experimental trajectories and simulations during gait

Maroger, Isabelle; Stasse, Olivier; Watier, Bruno

doi:10.1080/10255842.2020.1813421

Cited by 2 publications

(1 citation statement)

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“…Thus, holonomic locomotion models allow more degrees of freedom in the human locomotion, like the one described in Mombaur et al (2010), and should better describe human behaviour. Moreover, in Maroger et al (2020b) and Maroger et al (2020a), we already showed that an OC model adapted from Mombaur et al (2010) better fits human trajectories than a clothoid based model according to a metrics which evaluates the distance between generated trajectories and measurements on human subjects. Both models aimed to generate smooth CoM trajectories.…”

Section: State Of the Artmentioning

confidence: 93%

Inverse optimal control to model human trajectories during locomotion

Maroger

Stasse

Watier

2021

Computer Methods in Biomechanics and Biomedical Engineering

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Cobotic applications require a good knowledge of human behaviour in order to be cleverly, securely and fluidly performed. For example, to make a human and a humanoid robot perform a co-navigation or a co-manipulation task, a model of human walking trajectories is essential to make the robot follow or even anticipate the human movements. This paper aims to study the Center of Mass (CoM) path during locomotion and generate human-like trajectories with an optimal control scheme. It also proposes a metric which allows to assess this model compared to the human behaviour. CoM trajectories during locomotion of 10 healthy subjects were recorded and analysed as part of this study. Inverse optimal control was used to find the optimal cost function which best fits the model to the measurements. Then, the measurements and the generated data were compared in order to assess the performance of the presented model. Even if the experiments show a great variability in human behaviours, the model presented in this study gives an accurate approximation of the average human walking trajectories. Furthermore, this model gives an approximation of human locomotion good enough to improve cobotic tasks allowing a humanoid robot to anticipate the human behaviour.

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