A subject-specific kinematic model to predict human motion in exoskeleton-assisted gait

Diego Torricelli*, Camilo Cortés, Nerea Lete, Álvaro Bertelsen, Jose E. Gonzalez-Vargas, Antonio J. Del-Ama, Iris Dimbwadyo, Juan C. Moreno, Julian Florez, Jose L Pons

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number18
JournalFrontiers in Neurorobotics
Volume12
Issue numberAPR
DOIs
StatePublished - Apr 27 2018

Keywords

  • Benchmarking
  • Lower limb
  • Rehabilitation
  • Skeletal modeling
  • Walking
  • Wearable robot

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence

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