Infrared Thermography Temperature
Linked on 11/20/2023
The Infrared Thermography Temperature Dataset contains temperatures read from various locations of inferred images about patients, with the addition of oral temperatures measured for each individual. The 33 features consist of gender, age, ethnicity, ambiant temperature, humidity, distance, and other temperature readings from the thermal images. The dataset is intended to be used in a regression task to predict the oral temperature using the environment information as well as the thermal image readings.
Dataset Characteristics
Tabular
Subject Area
Health and Medicine
Associated Tasks
Regression
Feature Type
Real, Categorical
# Instances
1020
# Features
33
Dataset Information
Has Missing Values?
No
Introductory Paper
By Quanzeng Wang, Yangling Zhou, Pejman Ghassemi, David McBride, J. Casamento, T. Pfefer. 2021
Published in Italian National Conference on Sensors
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| SubjectID | ID | Categorical | Subject ID | no | ||
| aveOralF | Target | Continuous | Oral temperature measured in fast mode | no | ||
| aveOralM | Target | Continuous | Oral temperature measured in monitor mode | no | ||
| Gender | Feature | Categorical | Gender | Male or Female | no | |
| Age | Feature | Categorical | Age | Age ranges in categories | no | |
| Ethnicity | Feature | Categorical | Ethnicity | American Indian or Alaska Native, Asian, Black or African America, Hispanic/Latino, Multiracial, Native Hawaiian or other Pacific Islander, white. | no | |
| T_atm | Feature | Continuous | Ambiant temperature | no | ||
| Humidity | Feature | Continuous | Relative humidity | no | ||
| Distance | Feature | Continuous | Distance between the subjects and the IRTs. | no | ||
| T_offset1 | Feature | Continuous | Temperature difference between the set and measured blackbody temperature. See section 2.3.1 in https://doi.org/10.1117/1.JBO.25.9.097002 . | no |
0 to 10 of 36
Additional Variable Information
- gender - age - ethnicity - ambiant temperature - humidity - distance - temperature readings from the thermal images
Class Labels
The class labels for the regression task are either the oral temperature measured in fast mode (aveOralF) or the temperature measured in monitor mode (aveOralM).
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset infrared_thermography_temperature = fetch_ucirepo(id=925) # data (as pandas dataframes) X = infrared_thermography_temperature.data.features y = infrared_thermography_temperature.data.targets # metadata print(infrared_thermography_temperature.metadata) # variable information print(infrared_thermography_temperature.variables)
Wang,Quanzeng, Zhou,Yangling, Ghassemi,Pejman, McBride,David, Casamento,J., Pfefer,T., Wang,Quanzeng, Zhou,Yangling, Ghassemi,Pejman, McBride,David, Casamento,J., and Pfefer,T.. (2023). Infrared Thermography Temperature. UCI Machine Learning Repository. https://doi.org/10.13026/9ay4-2c37.
@misc{misc_infrared_thermography_temperature_925,
author = {Wang,Quanzeng, Zhou,Yangling, Ghassemi,Pejman, McBride,David, Casamento,J., Pfefer,T., Wang,Quanzeng, Zhou,Yangling, Ghassemi,Pejman, McBride,David, Casamento,J., and Pfefer,T.},
title = {{Infrared Thermography Temperature}},
year = {2023},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.13026/9ay4-2c37}
}Citations/Acknowledgements
If you use this dataset, please follow the acknowledgment policy on the original dataset website.
Keywords
Creators
Quanzeng Wang
Yangling Zhou
Pejman Ghassemi
David McBride
J. Casamento
T. Pfefer
Quanzeng Wang
Yangling Zhou
Pejman Ghassemi
David McBride
J. Casamento
T. Pfefer