{"title":"Development of an Assistive Device via Smart Glasses","authors":"Chia-Sui Wang, Wesley Huang, Yih-Feng Chang, Chia-Mao Yeh, Zhi-Yao Xu","doi":"10.1109/ECBIOS50299.2020.9203629","DOIUrl":null,"url":null,"abstract":"This study used smart glasses as the carrier to develop three applications aimed at the elderly. Taiwan entered an elderly society in 2018, with the proportion of the elderly population exceeding 14%. It is estimated that the elderly population will exceed 20% in 2026, and Taiwan will enter an ultra-elderly society. To improve the quality of life of the elderly and attach importance to the health care of the elderly, more and more scientific and technological products are used to improve the life and consumption patterns of the elderly, thereby promoting the popularization of smart wearable devices and sensing technologies. In view of the purpose, this research develops related functional applications with smart glasses. First, the memory recall mechanism is carried out based on face recognition. When the user uses the function, an embedded camera in glasses is turned on to capture the target personnel. Afterwards, a KNN (K Nearest Neighbor) machine learning approach is applied to identify the target, and the personal data is performed in the glasses through searching on the developed cloud database. Besides, the user can add, delete and modify the personal data through the computer, which the entrance is carried out by scanning the two-dimension code shown in the computer program with glasses for security purpose. Secondly, the image enhancement function shows that the captured texts can be expanded and enlarged in the projection monitor of glasses. Finally, the construction of voice commands is performed through the microphone within the suite of smart glasses. The results show that the accuracy rate of KNN face recognition is 93.3%, which can be applied to general life situations.","PeriodicalId":365765,"journal":{"name":"2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECBIOS50299.2020.9203629","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This study used smart glasses as the carrier to develop three applications aimed at the elderly. Taiwan entered an elderly society in 2018, with the proportion of the elderly population exceeding 14%. It is estimated that the elderly population will exceed 20% in 2026, and Taiwan will enter an ultra-elderly society. To improve the quality of life of the elderly and attach importance to the health care of the elderly, more and more scientific and technological products are used to improve the life and consumption patterns of the elderly, thereby promoting the popularization of smart wearable devices and sensing technologies. In view of the purpose, this research develops related functional applications with smart glasses. First, the memory recall mechanism is carried out based on face recognition. When the user uses the function, an embedded camera in glasses is turned on to capture the target personnel. Afterwards, a KNN (K Nearest Neighbor) machine learning approach is applied to identify the target, and the personal data is performed in the glasses through searching on the developed cloud database. Besides, the user can add, delete and modify the personal data through the computer, which the entrance is carried out by scanning the two-dimension code shown in the computer program with glasses for security purpose. Secondly, the image enhancement function shows that the captured texts can be expanded and enlarged in the projection monitor of glasses. Finally, the construction of voice commands is performed through the microphone within the suite of smart glasses. The results show that the accuracy rate of KNN face recognition is 93.3%, which can be applied to general life situations.