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Attention Score in Context
| Title |
Generation of a Movement Scheme for Positive Training
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2017
|
| DOI | 10.3389/fnins.2017.00096 |
| Pubmed ID | |
| Authors |
Lin Liu, Le Xie, Yun-Yong Shi, Bing-Chen An |
| Abstract |
Rehabilitation robots have been demonstrated to be an efficient tool in the field of rehabilitation training. Meanwhile, there are varieties of tasks designed for motion training. These tasks need to be transmitted to motion data for rehabilitation robots. In this paper, we designed a drinking task and captured the motion data as the ground truth, through sensors of an exoskeleton device named Neo-Arm. To verify the effectiveness of Neo-Arm, we used a Vicon system to capture the same motion task without Neo-Arm for comparison. Eight subjects participated in the experiment. The motion data of the drinking task, including the range of motion (ROM) and the velocity of each joint, are obtained. The result shows that the Neo-Arm can achieve the suitable precision and be fit for other kinds of upper limb motion tasks. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 1 | 10% |
| Student > Doctoral Student | 1 | 10% |
| Professor | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Student > Master | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 4 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 30% |
| Computer Science | 1 | 10% |
| Agricultural and Biological Sciences | 1 | 10% |
| Neuroscience | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 3 | 30% |
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 05 March 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#10,138
of 11,542 outputs
Outputs of similar age
#285,015
of 324,443 outputs
Outputs of similar age from Frontiers in Neuroscience
#186
of 208 outputs
Altmetric has tracked 25,382,440 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 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. 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 324,443 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 208 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.