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Temperature and Humidity Data Evaluation of Tight Sportswear during Motion Based on Intelligent Modeling Cover

Temperature and Humidity Data Evaluation of Tight Sportswear during Motion Based on Intelligent Modeling

Open Access
|Sep 2023

Figures & Tables

Fig. 1

Measurement points on human body
Measurement points on human body

Fig. 2

Experimental site and data acquisition
Experimental site and data acquisition

Fig. 3

Flow chart of LSTM model
Flow chart of LSTM model

Fig. 4.

LSTM model prediction results
LSTM model prediction results

Correlation between humidity in different parts

Abdomen humidity, %Back humidity, %Chest humidity, %Waist humidity, %
Spearman RhoAbdomen humidity, %Correlation coefficient1.0000.815**0.822**0.962**
Significance (two-tailed)·0.0000.0000.000
Back humidity, %Correlation coefficient0.815**1.0000.988**0.813**
Significance (two-tailed)0.000·0.0000.000
Chest humidity, %Correlation coefficient0.822**0.988**1.0000.819**
Significance (two-tailed)0.0000.000·0.000
Waist humidity, %Correlation coefficient0.962**0.813**0.819**1.000
Significance (two-tailed)0.0000.0000.000·

Fabric parameters of tight sportswear

TightsFabric compositionFabric structureWeight/ gom-2Thickness/ mmThread density/ longitudinal fabric density/coil number- (5cm)-1Thread density/ horizontal fabric density/coil numbero(5cm)-1
T17O%Polyester, 26%Nylon, 4%SpandexJersey stitch23O.80.66178.093.5
T286%Polyester, 14%SpandexWarp plain stitch200.60.91100.0185.0
T375%Polyester, 25%NylonWarp plain stitch181.10.6099.0103.5
T491%Polyester, 9%SpandexJersey stitch153.30.94136.588.5
T572%Polyester, 28%Spandex1×1 rib stitch159.10.7183.0148.0
T665% Polyamide, 35% ElastaneJersey stitch245.50.4890.5175.0
T781% Polyester, 19% Elastane1×1 rib stitch264.70.87121.5138.0

Correlation between temperature in different parts

Abdomen Temperature, °CBack temperature, °CChest temperature, °CWaist temperature, °C
Spearman RhoAbdomen temperature, °CCorrelation coefficient1.0000.838**0.0930.502**
Significance (two-tailed)·0.0000.3540.000
Back temperature, °CCorrelation coefficient0.838**1.0000.0840.339**
Significance (two-tailed)0.000·0.4010.001
Chest temperature, °CCorrelation coefficient0.0930.0841.0000.350**
Significance (two-tailed)0.3540.401·0.000
Correlation coefficient0.1010.1010.1010.101
Waist temperature, °CSignificance (two-tailed)0.502**0.339**0.350**1.000
Correlation coefficient0.0000.0010.000

Comparison of error values of prediction results of three neural network models

Comfort sensePrediction modelMAEMAPERMSE
A-humdBP neural network7.26820.33648.2078
RNN5.63140.18767.1911
LSTM4.96950.08145.6164
A-tempBP neural network6.35900.28387.5956
RNN5.17190.11236.0645
LSTM0.78810.02660.8719
B-humdBP neural network6.95880.31387.9893
RNN6.86720.30727.9201
LSTM2.62200.03533.8243
B-tempBP neural network3.25950.05174.4136
RNN1.99040.03010.7382
LSTM0.54240.01700.6483
C-humdBP neural network7.27770.34148.2873
RNN5.62930.17936.8152
LSTM3.34240.05204.5567
C-tempBP neural network4.08720.07135.2473
RNN2.71660.03693.9719
LSTM0.63560.02050.7686
DOI: https://doi.org/10.2478/ftee-2023-0021 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 1 - 8
Published on: Sep 1, 2023
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2023 Pengpeng Cheng, Jianping Wang, Xianyi Zeng, Pascal Bruniaux, Daoling Chen, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.