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Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, March 2016
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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5 tweeters

Citations

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4 Dimensions

Readers on

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82 Mendeley
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Title
Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study
Published in
Journal of NeuroEngineering and Rehabilitation, March 2016
DOI 10.1186/s12984-016-0142-9
Pubmed ID
Authors

Luca Lonini, Nicholas Shawen, Kathleen Scanlan, William Z. Rymer, Konrad P. Kording, Arun Jayaraman

Abstract

Clinical scores for evaluating walking skills with lower limb exoskeletons are often based on a single variable, such as distance walked or speed, even in cases where a host of features are measured. We investigated how to combine multiple features such that the resulting score has high discriminatory power, in particular with few patients. A new score is introduced that allows quantifying the walking ability of patients with spinal cord injury when using a powered exoskeleton. Four spinal cord injury patients were trained to walk over ground with the ReWalk™ exoskeleton. Body accelerations during use of the device were recorded by a wearable accelerometer and 4 features to evaluate walking skills were computed. The new score is the Gaussian naïve Bayes surprise, which evaluates patients relative to the features' distribution measured in 7 expert users of the ReWalk™. We compared our score based on all the features with a standard outcome measure, which is based on number of steps only. All 4 patients improved over the course of training, as their scores trended towards the expert users' scores. The combined score (Gaussian naïve surprise) was considerably more discriminative than the one using only walked distance (steps). At the end of training, 3 out of 4 patients were significantly different from the experts, according to the combined score (p < .001, Wilcoxon Signed-Rank Test). In contrast, all but one patient were scored as experts when number of steps was the only feature. Integrating multiple features could provide a more robust metric to measure patients' skills while they learn to walk with a robotic exoskeleton. Testing this approach with other features and more subjects remains as future work.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Canada 1 1%
Unknown 80 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 13 16%
Student > Master 13 16%
Student > Bachelor 12 15%
Unspecified 11 13%
Other 14 17%
Readers by discipline Count As %
Engineering 29 35%
Medicine and Dentistry 13 16%
Unspecified 12 15%
Nursing and Health Professions 7 9%
Neuroscience 7 9%
Other 14 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 April 2016.
All research outputs
#2,911,030
of 11,041,735 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#192
of 590 outputs
Outputs of similar age
#91,556
of 283,286 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#12
of 27 outputs
Altmetric has tracked 11,041,735 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 590 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 66% 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 283,286 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 67% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.