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A subject-specific kinematic model to predict human motion in exoskeleton-assisted gait
(Frontiers Media, 2018) Lete, N. (Nerea); del-Ama, A.J. (Antonio J.); Pons, J.L. (José L.); Torricelli, D. (Diego); Cortés, C. (Camilo); Moreno-Sastoque, J.C. (Juan C.); Bertelsen-Simonetti, A. (Álvaro); Florez, J. (Julian); Gonzalez-Vargas, J.E. (José E.); Dimbwadyo, I. (Iris)
The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5◦ globally, and around 1.5◦ at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.