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Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 6, p e0180315 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.
- Subjects :
- Male
Genetics and Molecular Biology (all)
Percentile
Research Validity
Physiology
lcsh:Medicine
Biosensing Techniques
Medicine (all)
Biochemistry, Genetics and Molecular Biology (all)
Agricultural and Biological Sciences (all)
Biochemistry
Body Temperature
0302 clinical medicine
Statistics
Abdomen
Medicine and Health Sciences
Adult
Aged
Equipment Design
Female
Humans
Middle Aged
Young Adult
Temperature
lcsh:Science
Mathematics
Multidisciplinary
05 social sciences
Healthy subjects
Thermal Conductivity
Research Assessment
Physiological Parameters
Thermocouples
Physical Sciences
Engineering and Technology
Anatomy
Research Article
Computer and Information Sciences
Mean squared error
Relation (database)
Materials by Structure
Thermometers
Materials Science
Material Properties
Equipment
Research and Analysis Methods
03 medical and health sciences
Reconstruction error
Position (vector)
Wireless
0501 psychology and cognitive sciences
050102 behavioral science & comparative psychology
Measurement Equipment
business.industry
lcsh:R
Real Time Computing
Biology and Life Sciences
Semiconductors
Reference values
lcsh:Q
business
Skin Temperature
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, PLoS ONE, Vol 12, Iss 6, p e0180315 (2017)
- Accession number :
- edsair.doi.dedup.....0a74453285efd2869ffed4a8b3a04216