{"title":"基于纳米纤维技术的智能情感感知服装系统的实现方法","authors":"Luo Qishu","doi":"10.35530/it.075.01.202379","DOIUrl":null,"url":null,"abstract":"The creation of smart clothing technologies now has more options because of the merging of fashion design, and\nwearable technology with nanofibre technology. This study suggests a means for putting a nanofibre-based, intelligent,\nemotion-aware clothing system into practice. By recognizing and reacting to the wearer's psychological state, the system\nseeks to improve user convenience and well-being. In this study, a unique, self-sufficient weight-tuned Kohonen neural\nnetwork (SW-KNN) method is used to categorize emotional states. To determine the wearer's emotional state, we first\ncollect a dataset of signals from the body, including pulse, body temperature, and perspiration production. The dataset\nis then added to the preprocessing stage, where the raw data is normalized using the min-max method. The important\nfeatures from the cleaned data are then extracted using the Fast Fourier Transform (FFT). The smart control unit\nprocesses the physiological signals that have been acquired. The proposed approach is utilized to categorize the\nwearer's emotional state, and the white shark optimization (WSO) approach is used to improve the classification\naccuracy. The control unit has a microchip and wireless connectivity abilities, enabling it to send the devices’ connected\ndevices the classified emotional status. The clothing technology can continuously modify its features based on the\nidentified emotional state to enhance the wearer's comfort. The findings of the study stated that the proposed technique\nhas provided accuracy and precision of 97.8% and 98.1% respectively.","PeriodicalId":13638,"journal":{"name":"Industria Textila","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation method of intelligent emotion-aware clothing system\\nbased on nanofibre technology\",\"authors\":\"Luo Qishu\",\"doi\":\"10.35530/it.075.01.202379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The creation of smart clothing technologies now has more options because of the merging of fashion design, and\\nwearable technology with nanofibre technology. This study suggests a means for putting a nanofibre-based, intelligent,\\nemotion-aware clothing system into practice. By recognizing and reacting to the wearer's psychological state, the system\\nseeks to improve user convenience and well-being. In this study, a unique, self-sufficient weight-tuned Kohonen neural\\nnetwork (SW-KNN) method is used to categorize emotional states. To determine the wearer's emotional state, we first\\ncollect a dataset of signals from the body, including pulse, body temperature, and perspiration production. The dataset\\nis then added to the preprocessing stage, where the raw data is normalized using the min-max method. The important\\nfeatures from the cleaned data are then extracted using the Fast Fourier Transform (FFT). The smart control unit\\nprocesses the physiological signals that have been acquired. The proposed approach is utilized to categorize the\\nwearer's emotional state, and the white shark optimization (WSO) approach is used to improve the classification\\naccuracy. The control unit has a microchip and wireless connectivity abilities, enabling it to send the devices’ connected\\ndevices the classified emotional status. The clothing technology can continuously modify its features based on the\\nidentified emotional state to enhance the wearer's comfort. The findings of the study stated that the proposed technique\\nhas provided accuracy and precision of 97.8% and 98.1% respectively.\",\"PeriodicalId\":13638,\"journal\":{\"name\":\"Industria Textila\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Industria Textila\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.35530/it.075.01.202379\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, TEXTILES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industria Textila","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.35530/it.075.01.202379","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, TEXTILES","Score":null,"Total":0}
Implementation method of intelligent emotion-aware clothing system
based on nanofibre technology
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.
期刊介绍:
Industria Textila journal is addressed to university and research specialists, to companies active in the textiles and clothing sector and to the related sectors users of textile products with a technical purpose.