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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data
|
|---|---|
| Published in |
Artificial Intelligence in Engineering, April 2023
|
| DOI | 10.1016/j.aei.2023.101978 |
| Authors |
Imran Mehmood, Heng Li, Yazan Qarout, Waleed Umer, Shahnawaz Anwer, Haitao Wu, Mudasir Hussain, Maxwell Fordjour Antwi-Afari |
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 % |
|---|---|---|
| United Kingdom | 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 41 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 15% |
| Student > Doctoral Student | 6 | 15% |
| Student > Bachelor | 3 | 7% |
| Student > Ph. D. Student | 3 | 7% |
| Lecturer | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 16 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 24% |
| Unspecified | 6 | 15% |
| Computer Science | 3 | 7% |
| Agricultural and Biological Sciences | 1 | 2% |
| Business, Management and Accounting | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 18 | 44% |
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 19 May 2023.
All research outputs
#15,533,143
of 25,394,764 outputs
Outputs from Artificial Intelligence in Engineering
#249
of 377 outputs
Outputs of similar age
#199,855
of 421,804 outputs
Outputs of similar age from Artificial Intelligence in Engineering
#2
of 3 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 377 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 31st percentile – i.e., 31% 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 421,804 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 51% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.