{"title":"使用视觉变压器进行跌落事件检测","authors":"Ankita Dey, S. Rajan, George Xiao, Jianping Lu","doi":"10.1109/SENSORS52175.2022.9967352","DOIUrl":null,"url":null,"abstract":"Privacy-preserving radar-based fall detection for older adults is becoming essential as falls in adults above 65 years of age may result in death or a permanent physical disability. In this paper, a novel deep learning-based fall event detection technique using Vision Transformers with Shifted Patch Tokenization and Locality Self Attention is proposed. The proposed approach is evaluated using publicly available dataset. Preliminary evaluation shows improved performance over transfer learning models and standard Vision Transformer.","PeriodicalId":120357,"journal":{"name":"2022 IEEE Sensors","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fall Event Detection using Vision Transformer\",\"authors\":\"Ankita Dey, S. Rajan, George Xiao, Jianping Lu\",\"doi\":\"10.1109/SENSORS52175.2022.9967352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Privacy-preserving radar-based fall detection for older adults is becoming essential as falls in adults above 65 years of age may result in death or a permanent physical disability. In this paper, a novel deep learning-based fall event detection technique using Vision Transformers with Shifted Patch Tokenization and Locality Self Attention is proposed. The proposed approach is evaluated using publicly available dataset. Preliminary evaluation shows improved performance over transfer learning models and standard Vision Transformer.\",\"PeriodicalId\":120357,\"journal\":{\"name\":\"2022 IEEE Sensors\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SENSORS52175.2022.9967352\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SENSORS52175.2022.9967352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Privacy-preserving radar-based fall detection for older adults is becoming essential as falls in adults above 65 years of age may result in death or a permanent physical disability. In this paper, a novel deep learning-based fall event detection technique using Vision Transformers with Shifted Patch Tokenization and Locality Self Attention is proposed. The proposed approach is evaluated using publicly available dataset. Preliminary evaluation shows improved performance over transfer learning models and standard Vision Transformer.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
