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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems
|
|---|---|
| Published in |
BioMedical Engineering OnLine, February 2012
|
| DOI | 10.1186/1475-925x-11-9 |
| Pubmed ID | |
| Authors |
Mitchell Yuwono, Bruce D Moulton, Steven W Su, Branko G Celler, Hung T Nguyen |
| Abstract |
Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. |
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 % |
|---|---|---|
| Mexico | 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 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| Italy | 1 | <1% |
| Australia | 1 | <1% |
| Hong Kong | 1 | <1% |
| Brazil | 1 | <1% |
| Qatar | 1 | <1% |
| Unknown | 135 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 17% |
| Student > Master | 22 | 15% |
| Researcher | 19 | 13% |
| Student > Bachelor | 19 | 13% |
| Student > Doctoral Student | 10 | 7% |
| Other | 23 | 16% |
| Unknown | 25 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 38 | 27% |
| Computer Science | 30 | 21% |
| Medicine and Dentistry | 14 | 10% |
| Nursing and Health Professions | 6 | 4% |
| Agricultural and Biological Sciences | 4 | 3% |
| Other | 19 | 13% |
| Unknown | 32 | 22% |
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 16 February 2012.
All research outputs
#18,304,874
of 22,663,150 outputs
Outputs from BioMedical Engineering OnLine
#564
of 821 outputs
Outputs of similar age
#120,642
of 155,000 outputs
Outputs of similar age from BioMedical Engineering OnLine
#4
of 10 outputs
Altmetric has tracked 22,663,150 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 821 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 15th percentile – i.e., 15% 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 155,000 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.