| Title |
Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, June 2012
|
| DOI | 10.1186/1743-0003-9-32 |
| Pubmed ID | |
| Authors |
Katie L Meadmore, Ann-Marie Hughes, Chris T Freeman, Zhonglun Cai, Daisy Tong, Jane H Burridge, Eric Rogers |
| Abstract |
Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 226 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 1% |
| Canada | 2 | <1% |
| United States | 2 | <1% |
| Mexico | 1 | <1% |
| Switzerland | 1 | <1% |
| Unknown | 217 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 39 | 17% |
| Student > Master | 38 | 17% |
| Student > Bachelor | 25 | 11% |
| Researcher | 22 | 10% |
| Student > Doctoral Student | 15 | 7% |
| Other | 40 | 18% |
| Unknown | 47 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 53 | 23% |
| Nursing and Health Professions | 34 | 15% |
| Medicine and Dentistry | 30 | 13% |
| Psychology | 10 | 4% |
| Neuroscience | 10 | 4% |
| Other | 35 | 15% |
| Unknown | 54 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 November 2013.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#811
of 1,413 outputs
Outputs of similar age
#110,355
of 180,772 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#7
of 12 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,413 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 38th percentile – i.e., 38% 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 180,772 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.