↓ Skip to main content

Comparison between sEMG and force as control interfaces to support planar arm movements in adults with Duchenne: a feasibility study

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, July 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
99 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Comparison between sEMG and force as control interfaces to support planar arm movements in adults with Duchenne: a feasibility study
Published in
Journal of NeuroEngineering and Rehabilitation, July 2017
DOI 10.1186/s12984-017-0282-6
Pubmed ID
Authors

Joan Lobo-Prat, Kostas Nizamis, Mariska M.H.P. Janssen, Arvid Q.L. Keemink, Peter H. Veltink, Bart F.J.M. Koopman, Arno H.A. Stienen

Abstract

Adults with Duchenne muscular dystrophy (DMD) can benefit from devices that actively support their arm function. A critical component of such devices is the control interface as it is responsible for the human-machine interaction. Our previous work indicated that surface electromyography (sEMG) and force-based control with active gravity and joint-stiffness compensation were feasible solutions for the support of elbow movements (one degree of freedom). In this paper, we extend the evaluation of sEMG- and force-based control interfaces to simultaneous and proportional control of planar arm movements (two degrees of freedom). Three men with DMD (18-23 years-old) with different levels of arm function (i.e. Brooke scores of 4, 5 and 6) performed a series of line-tracing tasks over a tabletop surface using an experimental active arm support. The arm movements were controlled using three control methods: sEMG-based control, force-based control with stiffness compensation (FSC), and force-based control with no compensation (FNC). The movement performance was evaluated in terms of percentage of task completion, tracing error, smoothness and speed. For subject S1 (Brooke 4) FNC was the preferred method and performed better than FSC and sEMG. FNC was not usable for subject S2 (Brooke 5) and S3 (Brooke 6). Subject S2 presented significantly lower movement speed with sEMG than with FSC, yet he preferred sEMG since FSC was perceived to be too fatiguing. Subject S3 could not successfully use neither of the two force-based control methods, while with sEMG he could reach almost his entire workspace. Movement performance and subjective preference of the three control methods differed with the level of arm function of the participants. Our results indicate that all three control methods have to be considered in real applications, as they present complementary advantages and disadvantages. The fact that the two weaker subjects (S2 and S3) experienced the force-based control interfaces as fatiguing suggests that sEMG-based control interfaces could be a better solution for adults with DMD. Yet force-based control interfaces can be a better alternative for those cases in which voluntary forces are higher than the stiffness forces of the arms.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 20%
Researcher 15 15%
Student > Bachelor 8 8%
Student > Ph. D. Student 7 7%
Student > Postgraduate 4 4%
Other 13 13%
Unknown 32 32%
Readers by discipline Count As %
Engineering 19 19%
Medicine and Dentistry 11 11%
Nursing and Health Professions 10 10%
Computer Science 6 6%
Sports and Recreations 4 4%
Other 11 11%
Unknown 38 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 14 July 2017.
All research outputs
#20,434,884
of 22,988,380 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#1,146
of 1,289 outputs
Outputs of similar age
#272,528
of 312,615 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#25
of 27 outputs
Altmetric has tracked 22,988,380 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,289 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 312,615 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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 is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.