Temperature and Humidity Data Evaluation of Tight Sportswear during Motion Based on Intelligent Modeling
, , , e | 01 set 2023
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Article Category: Research Article
Pubblicato online: 01 set 2023
Pagine: 1 - 8
DOI: https://doi.org/10.2478/ftee-2023-0021
Parole chiave
© 2023 Łukasiewicz Research Network-Łódź Institute of Technology, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
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Fig. 4.
Correlation between humidity in different parts
| Abdomen humidity, % | Back humidity, % | Chest humidity, % | Waist humidity, % | |||
|---|---|---|---|---|---|---|
| Spearman Rho | Abdomen humidity, % | Correlation coefficient | 1.000 | 0.815** | 0.822** | 0.962** |
| Significance (two-tailed) | · | 0.000 | 0.000 | 0.000 | ||
| Back humidity, % | Correlation coefficient | 0.815** | 1.000 | 0.988** | 0.813** | |
| Significance (two-tailed) | 0.000 | · | 0.000 | 0.000 | ||
| Chest humidity, % | Correlation coefficient | 0.822** | 0.988** | 1.000 | 0.819** | |
| Significance (two-tailed) | 0.000 | 0.000 | · | 0.000 | ||
| Waist humidity, % | Correlation coefficient | 0.962** | 0.813** | 0.819** | 1.000 | |
| Significance (two-tailed) | 0.000 | 0.000 | 0.000 | · | ||
Fabric parameters of tight sportswear
| Tights | Fabric composition | Fabric structure | Weight/ gom-2 | Thickness/ mm | Thread density/ longitudinal fabric density/coil number- (5cm)-1 | Thread density/ horizontal fabric density/coil numbero(5cm)-1 |
|---|---|---|---|---|---|---|
| T1 | 7O%Polyester, 26%Nylon, 4%Spandex | Jersey stitch | 23O.8 | 0.66 | 178.0 | 93.5 |
| T2 | 86%Polyester, 14%Spandex | Warp plain stitch | 200.6 | 0.91 | 100.0 | 185.0 |
| T3 | 75%Polyester, 25%Nylon | Warp plain stitch | 181.1 | 0.60 | 99.0 | 103.5 |
| T4 | 91%Polyester, 9%Spandex | Jersey stitch | 153.3 | 0.94 | 136.5 | 88.5 |
| T5 | 72%Polyester, 28%Spandex | 1×1 rib stitch | 159.1 | 0.71 | 83.0 | 148.0 |
| T6 | 65% Polyamide, 35% Elastane | Jersey stitch | 245.5 | 0.48 | 90.5 | 175.0 |
| T7 | 81% Polyester, 19% Elastane | 1×1 rib stitch | 264.7 | 0.87 | 121.5 | 138.0 |
Correlation between temperature in different parts
| Abdomen Temperature, °C | Back temperature, °C | Chest temperature, °C | Waist temperature, °C | |||
|---|---|---|---|---|---|---|
| Spearman Rho | Abdomen temperature, °C | Correlation coefficient | 1.000 | 0.838** | 0.093 | 0.502** |
| Significance (two-tailed) | · | 0.000 | 0.354 | 0.000 | ||
| Back temperature, °C | Correlation coefficient | 0.838** | 1.000 | 0.084 | 0.339** | |
| Significance (two-tailed) | 0.000 | · | 0.401 | 0.001 | ||
| Chest temperature, °C | Correlation coefficient | 0.093 | 0.084 | 1.000 | 0.350** | |
| Significance (two-tailed) | 0.354 | 0.401 | · | 0.000 | ||
| Correlation coefficient | 0.101 | 0.101 | 0.101 | 0.101 | ||
| Waist temperature, °C | Significance (two-tailed) | 0.502** | 0.339** | 0.350** | 1.000 | |
| Correlation coefficient | 0.000 | 0.001 | 0.000 | |||
Comparison of error values of prediction results of three neural network models
| Comfort sense | Prediction model | MAE | MAPE | RMSE |
|---|---|---|---|---|
| A-humd | BP neural network | 7.2682 | 0.3364 | 8.2078 |
| RNN | 5.6314 | 0.1876 | 7.1911 | |
| LSTM | 4.9695 | 0.0814 | 5.6164 | |
| A-temp | BP neural network | 6.3590 | 0.2838 | 7.5956 |
| RNN | 5.1719 | 0.1123 | 6.0645 | |
| LSTM | 0.7881 | 0.0266 | 0.8719 | |
| B-humd | BP neural network | 6.9588 | 0.3138 | 7.9893 |
| RNN | 6.8672 | 0.3072 | 7.9201 | |
| LSTM | 2.6220 | 0.0353 | 3.8243 | |
| B-temp | BP neural network | 3.2595 | 0.0517 | 4.4136 |
| RNN | 1.9904 | 0.0301 | 0.7382 | |
| LSTM | 0.5424 | 0.0170 | 0.6483 | |
| C-humd | BP neural network | 7.2777 | 0.3414 | 8.2873 |
| RNN | 5.6293 | 0.1793 | 6.8152 | |
| LSTM | 3.3424 | 0.0520 | 4.5567 | |
| C-temp | BP neural network | 4.0872 | 0.0713 | 5.2473 |
| RNN | 2.7166 | 0.0369 | 3.9719 | |
| LSTM | 0.6356 | 0.0205 | 0.7686 |