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A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Overview of attention for article published in Frontiers in Neurorobotics, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Title
A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait
Published in
Frontiers in Neurorobotics, April 2018
DOI 10.3389/fnbot.2018.00018
Pubmed ID
Authors

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

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.

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X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 138 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 19%
Researcher 22 16%
Student > Master 21 15%
Student > Bachelor 13 9%
Student > Doctoral Student 8 6%
Other 13 9%
Unknown 35 25%
Readers by discipline Count As %
Engineering 72 52%
Computer Science 6 4%
Nursing and Health Professions 6 4%
Neuroscience 3 2%
Medicine and Dentistry 2 1%
Other 5 4%
Unknown 44 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2021.
All research outputs
#2,978,754
of 23,043,346 outputs
Outputs from Frontiers in Neurorobotics
#61
of 881 outputs
Outputs of similar age
#64,311
of 326,464 outputs
Outputs of similar age from Frontiers in Neurorobotics
#1
of 22 outputs
Altmetric has tracked 23,043,346 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 881 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,464 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.