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Robot-aided assessment of lower extremity functions: a review

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, August 2016
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  • High Attention Score compared to outputs of the same age and source (85th percentile)

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7 X users

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75 Dimensions

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309 Mendeley
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Title
Robot-aided assessment of lower extremity functions: a review
Published in
Journal of NeuroEngineering and Rehabilitation, August 2016
DOI 10.1186/s12984-016-0180-3
Pubmed ID
Authors

Serena Maggioni, Alejandro Melendez-Calderon, Edwin van Asseldonk, Verena Klamroth-Marganska, Lars Lünenburger, Robert Riener, Herman van der Kooij

Abstract

The assessment of sensorimotor functions is extremely important to understand the health status of a patient and its change over time. Assessments are necessary to plan and adjust the therapy in order to maximize the chances of individual recovery. Nowadays, however, assessments are seldom used in clinical practice due to administrative constraints or to inadequate validity, reliability and responsiveness. In clinical trials, more sensitive and reliable measurement scales could unmask changes in physiological variables that would not be visible with existing clinical scores.In the last decades robotic devices have become available for neurorehabilitation training in clinical centers. Besides training, robotic devices can overcome some of the limitations in traditional clinical assessments by providing more objective, sensitive, reliable and time-efficient measurements. However, it is necessary to understand the clinical needs to be able to develop novel robot-aided assessment methods that can be integrated in clinical practice.This paper aims at providing researchers and developers in the field of robotic neurorehabilitation with a comprehensive review of assessment methods for the lower extremities. Among the ICF domains, we included those related to lower extremities sensorimotor functions and walking; for each chapter we present and discuss existing assessments used in routine clinical practice and contrast those to state-of-the-art instrumented and robot-aided technologies. Based on the shortcomings of current assessments, on the identified clinical needs and on the opportunities offered by robotic devices, we propose future directions for research in rehabilitation robotics. The review and recommendations provided in this paper aim to guide the design of the next generation of robot-aided functional assessments, their validation and their translation to clinical practice.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 309 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 308 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 66 21%
Student > Ph. D. Student 50 16%
Researcher 35 11%
Student > Bachelor 30 10%
Student > Doctoral Student 19 6%
Other 39 13%
Unknown 70 23%
Readers by discipline Count As %
Engineering 108 35%
Medicine and Dentistry 30 10%
Nursing and Health Professions 26 8%
Neuroscience 15 5%
Sports and Recreations 10 3%
Other 33 11%
Unknown 87 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 29 November 2016.
All research outputs
#6,993,507
of 25,459,177 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#397
of 1,416 outputs
Outputs of similar age
#112,031
of 381,786 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#4
of 21 outputs
Altmetric has tracked 25,459,177 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,416 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 71% 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 381,786 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.