Implementation method of intelligent emotion-aware clothing system based on nanofibre technology

Abstract

The creation of smart clothing technologies now has more options because of the merging of fashion design, and wearable technology with nanofibre technology. This study suggests a means for putting a nanofibre-based, intelligent, emotion-aware clothing system into practice. By recognizing and reacting to the wearer's psychological state, the system seeks to improve user convenience and well-being. In this study, a unique, self-sufficient weight-tuned Kohonen neural network (SW-KNN) method is used to categorize emotional states. To determine the wearer's emotional state, we first collect a dataset of signals from the body, including pulse, body temperature, and perspiration production. The dataset is then added to the preprocessing stage, where the raw data is normalized using the min-max method. The important features from the cleaned data are then extracted using the Fast Fourier Transform (FFT). The smart control unit processes the physiological signals that have been acquired. The proposed approach is utilized to categorize the wearer's emotional state, and the white shark optimization (WSO) approach is used to improve the classification accuracy. The control unit has a microchip and wireless connectivity abilities, enabling it to send the devices’ connected devices the classified emotional status. The clothing technology can continuously modify its features based on the identified emotional state to enhance the wearer's comfort. The findings of the study stated that the proposed technique has provided accuracy and precision of 97.8% and 98.1% respectively.