Analysis And Investigation Of Temperature Field Between Human Body And Seat Contact Surface

With the advancement of technology level, the improvement of life styles and the promotion of work intensity, the demand for seat thermal comfort is becoming increasingly high in daily work and life. It is beneficial to alleviate body fatigue and improve the efficiency of work while taking a comfortable chair.It is quite significant to study temperature field between human body and seat contact surface for improving the seat thermal comfort, as the temperature is the key factor that affects the thermal comfort.According to the previously investigative literature, temperature field data between human body and seat contact surface were collected by using the temperature acquisition equipment in this paper. Through data processing such as smoothing filter of EMD and 3D imaging, and certain predictive analysis such as Decision Tree, an objective evaluation and prediction approach based on the variation of temperature field was applied. In addition, the results of analyzing temperature field and solving practical problems were discussed.In this paper, the number and arrangement of sensors array between human body and seat contact surface was analyzed. A temperature field acquisition system was designed which measures the temperature data and saves them to PC's hard drive. According to the characteristics of experimental data, EMD filter for data preprocessing was designed. The aim is to smooth raw data and apply processed data to subsequent analysis. Based on this pretreatment, the auto regression prediction model was employed to analyze and train the data. Based on the Decision Tree algorithm, the unknown data were predicted by the known part of the section. Finally, MATLAB was used to simulate and analyze. From the perspective of chair users, it is easy to make decision in advance, timely to adjust the sitting posture, and to replace the seat for achieving the best caringeffect. According to experimental sets and requirements, collected data were mainly analyzed and predicted based on Decision Tree about Autoregressive predict model. Root mean square errors of within group and between groups were discussed. Furthermore, the feasibility and accuracy analysis of prediction method and AR model were evaluated and verified in terms of solving the temperature field prediction problem.The experimental results show that, the average RMSE is less than 0.4 ?within the group, which proves that the AR model can provide the predictive ability of the acceptable accuracy, through the repeated predicted experiment of future 15 minutes. The cross experiment result also confirmed that the prediction accuracy is 0.47±0.06?.