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

Cheng Pengpeng, Wang Jianping, Zeng Xianyi, Bruniaux Pascal, Chen Daoling

Fibres & Textiles in Eastern Europe

2023

Vol. 31, no. 3

1--8

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.

Rat robot motion state identification based on a wearable inertial sensor

Chen Yuxin, Xu Haoze, Yang Wei, Yang Canjun, Xu Kedi

Metrology and Measurement Systems

2021

Vol. 28, nr 2

255--268

Rat robots have great potential in rescue and search tasks because of their excellent motion ability. However, most of the current rat-robot systems relay on human guidance due to variable voluntary motor behaviour of rats, which limits their application. In this study, we developed a real-time system to detect a rat robot’s transient motion states, as the prerequisite for further study of automatic navigation. We built the detection model by using a wearable inertial sensor to capture acceleration and angular velocity data during the control of a rat robot. Various machine learning algorithms, including Decision Trees, Random Forests, Logistic Regression, and Support Vector Machines, were employed to perform the classification of motion states. This detection system was tested in manual navigation experiments, with detection accuracy achieving 96.70%. The sequence of transient motion states could be further used as a promising reference for offline behaviour analysis.