- Tytuł:
- Temperature and Humidity Data Evaluation of Tight Sportswear During Motion Based on Intelligent Modeling
- Autorzy:
-
Cheng, Pengpeng
Wang, Jianping
Zeng, Xianyi
Bruniaux, Pascal
Chen, Daoling - Powiązania:
- https://bibliotekanauki.pl/articles/24200969.pdf
- Data publikacji:
- 2023
- Wydawca:
- Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
- Tematy:
-
motion state
tight sportswear
temperature
humidity
prediction model - Opis:
- A neural network structure of Long Short Term Memory (LSTM) is proposed which could be used to predict the temperaturę and humidity of other key parts from the temperature and humidity data of some parts of the human body when wearing tight sportswear, so as to realize the temperature and humidity data prediction of all key points of the human body. The temperaturę and humidity of different people wearing tights were collected by DHT sensors. The experimental results show that the LSTM neural network structure proposed has higher prediction accuracy than other algorithms, and the model evaluates the feasibility of temperature and humidity data of tights in a state of motion, which facilitates the study of dynamic thermal and humid comfort and reduces the time cost of analyzing the temperature and humidity distribution and changing the law during human movement. It will effectively promote the study of temperature and humidity changes when people wear sports tights, provide theoretical reference for the study of human skin temperature in the field of sports medicine, and provide practical guidance for the application of human skin temperature changes in sports clothing production, diagnosis and prevention of sports injuries.
- Źródło:
-
Fibres & Textiles in Eastern Europe; 2023, 31, 3; 1--8
1230-3666
2300-7354 - Pojawia się w:
- Fibres & Textiles in Eastern Europe
- Dostawca treści:
- Biblioteka Nauki
Artykuł