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An unsupervised approach to activity recognition and segmentation based on object-use fingerprints

Overview of attention for article published in Data & Knowledge Engineering, June 2010
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About this Attention Score

  • Among the highest-scoring outputs from this source (#38 of 261)
  • Above-average Attention Score compared to outputs of the same age (62nd percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
80 Dimensions

Readers on

mendeley
74 Mendeley
citeulike
2 CiteULike
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Title
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Published in
Data & Knowledge Engineering, June 2010
DOI 10.1016/j.datak.2010.01.004
Authors

Tao Gu, Shaxun Chen, Xianping Tao, Jian Lu

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 2 3%
France 1 1%
Malaysia 1 1%
Slovenia 1 1%
Turkey 1 1%
Korea, Republic of 1 1%
Singapore 1 1%
Unknown 64 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 36%
Researcher 11 15%
Student > Master 11 15%
Student > Doctoral Student 10 14%
Professor 3 4%
Other 12 16%
Readers by discipline Count As %
Computer Science 54 73%
Engineering 12 16%
Unspecified 6 8%
Psychology 1 1%
Design 1 1%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 March 2012.
All research outputs
#3,506,445
of 12,243,011 outputs
Outputs from Data & Knowledge Engineering
#38
of 261 outputs
Outputs of similar age
#96,830
of 261,695 outputs
Outputs of similar age from Data & Knowledge Engineering
#2
of 4 outputs
Altmetric has tracked 12,243,011 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 261 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 25th percentile – i.e., 25% 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 261,695 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 62% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.