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Attention Score in Context
| Title |
An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal
|
|---|---|
| Published in |
Frontiers in Neurology, November 2017
|
| DOI | 10.3389/fneur.2017.00573 |
| Pubmed ID | |
| Authors |
Huijun Ding, Qing He, Yongjin Zhou, Guo Dan, Song Cui |
| Abstract |
Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human-computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results. |
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 % |
|---|---|---|
| Switzerland | 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Student > Bachelor | 6 | 19% |
| Researcher | 5 | 16% |
| Student > Master | 2 | 6% |
| Lecturer | 1 | 3% |
| Other | 5 | 16% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 29% |
| Computer Science | 8 | 26% |
| Social Sciences | 3 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Psychology | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 6 | 19% |
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 09 November 2017.
All research outputs
#20,451,991
of 23,007,887 outputs
Outputs from Frontiers in Neurology
#8,927
of 11,904 outputs
Outputs of similar age
#288,901
of 331,430 outputs
Outputs of similar age from Frontiers in Neurology
#152
of 213 outputs
Altmetric has tracked 23,007,887 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 11,904 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 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 331,430 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 213 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.