Research on Comfort Measuring Device and Evaluation Algorithm of Clothing

Smart clothing can monitor the wearer's physical condition in real time or protect the human body during special work. Clothing pressure, temperature, humidity, and the wearer's heart rate are important indexes of clothing comfort evaluation. Based on the low-power chip MSP430F149, a measurement system capable of simultaneously collecting these four parameters and a clothing comfort evaluation method based on these four parameters was developed. Thirty subjects were selected to measure four parameters under different postures. The collected ECG signal was filtered by the empirical mode decomposition, and the adaptive threshold detection algorithm improved the R wave detection efficiency. The fuzzy C-means clustering based on improved kernel function algorithm was used to intelligently analyze the average clothing pressure, extreme pressure difference, extreme temperature difference, extreme humidity difference and change rate of wearer's heart. The research results show that the system has the characteristics of low power consumption and portability. The accuracy of temperature, humidity, and heart rate is high. The proposed evaluation method can objectively and accurately evaluate the comfort of clothing and direct the clothing design.