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A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study

Overview of attention for article published in European Journal of Medical Research, May 2018
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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4 X users

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28 Dimensions

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100 Mendeley
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Title
A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
Published in
European Journal of Medical Research, May 2018
DOI 10.1186/s40001-018-0326-9
Pubmed ID
Authors

Richard M. Kwasnicki, George W. V. Cross, Luke Geoghegan, Zhiqiang Zhang, Peter Reilly, Ara Darzi, Guang Zhong Yang, Roger Emery

Abstract

The prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity. Sixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a 'Posture' protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an 'Activity' protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a 'Proof of Concept' model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations. The WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application. The study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 23%
Student > Master 13 13%
Student > Bachelor 11 11%
Researcher 8 8%
Student > Doctoral Student 6 6%
Other 8 8%
Unknown 31 31%
Readers by discipline Count As %
Engineering 14 14%
Medicine and Dentistry 11 11%
Nursing and Health Professions 8 8%
Computer Science 6 6%
Sports and Recreations 4 4%
Other 20 20%
Unknown 37 37%
Attention Score in Context

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 01 June 2018.
All research outputs
#14,283,318
of 25,382,440 outputs
Outputs from European Journal of Medical Research
#304
of 923 outputs
Outputs of similar age
#165,570
of 344,275 outputs
Outputs of similar age from European Journal of Medical Research
#3
of 8 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 923 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one has gotten more attention than average, scoring higher than 66% 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 344,275 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 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.