↓ Skip to main content

Problems of video-based pain detection in patients with dementia: a road map to an interdisciplinary solution

Overview of attention for article published in BMC Geriatrics, January 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
75 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Problems of video-based pain detection in patients with dementia: a road map to an interdisciplinary solution
Published in
BMC Geriatrics, January 2017
DOI 10.1186/s12877-017-0427-2
Pubmed ID
Authors

Miriam Kunz, Dominik Seuss, Teena Hassan, Jens U. Garbas, Michael Siebers, Ute Schmid, Michael Schöberl, Stefan Lautenbacher

Abstract

Given the unreliable self-report in patients with dementia, pain assessment should also rely on the observation of pain behaviors, such as facial expressions. Ideal observers should be well trained and should observe the patient continuously in order to pick up any pain-indicative behavior; which are requisitions beyond realistic possibilities of pain care. Therefore, the need for video-based pain detection systems has been repeatedly voiced. Such systems would allow for constant monitoring of pain behaviors and thereby allow for a timely adjustment of pain management in these fragile patients, who are often undertreated for pain. In this road map paper we describe an interdisciplinary approach to develop such a video-based pain detection system. The development starts with the selection of appropriate video material of people in pain as well as the development of technical methods to capture their faces. Furthermore, single facial motions are automatically extracted according to an international coding system. Computer algorithms are trained to detect the combination and timing of those motions, which are pain-indicative. We hope to encourage colleagues to join forces and to inform end-users about an imminent solution of a pressing pain-care problem. For the near future, implementation of such systems can be foreseen to monitor immobile patients in intensive and postoperative care situations.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 20%
Student > Ph. D. Student 12 16%
Researcher 8 11%
Student > Bachelor 7 9%
Student > Postgraduate 5 7%
Other 11 15%
Unknown 17 23%
Readers by discipline Count As %
Psychology 11 15%
Computer Science 11 15%
Nursing and Health Professions 7 9%
Engineering 6 8%
Medicine and Dentistry 6 8%
Other 11 15%
Unknown 23 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 21 June 2017.
All research outputs
#1,225,427
of 11,394,845 outputs
Outputs from BMC Geriatrics
#221
of 1,056 outputs
Outputs of similar age
#54,577
of 321,219 outputs
Outputs of similar age from BMC Geriatrics
#14
of 45 outputs
Altmetric has tracked 11,394,845 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,056 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done well, scoring higher than 78% of its peers.
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 321,219 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.