R. Degraeve, J. Doevenspeck, A. Fantini, P. Debacker, D. Linten, D. Verkest
{"title":"基于随机oxrram时间序列机器学习的步态识别","authors":"R. Degraeve, J. Doevenspeck, A. Fantini, P. Debacker, D. Linten, D. Verkest","doi":"10.23919/VLSIT.2019.8776571","DOIUrl":null,"url":null,"abstract":"The way a person walks, i.e. his/her gait, can be as unique as a fingerprint. With portable accelerometers and/or gyroscopes available in present-day smartphones, gait verification and identification can be exploited for low-level security [1]. Achieving this requires machine learning of a time sequence.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"10 1","pages":"T84-T85"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Gait identification using stochastic OXRRAM-based time sequence machine learning\",\"authors\":\"R. Degraeve, J. Doevenspeck, A. Fantini, P. Debacker, D. Linten, D. Verkest\",\"doi\":\"10.23919/VLSIT.2019.8776571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The way a person walks, i.e. his/her gait, can be as unique as a fingerprint. With portable accelerometers and/or gyroscopes available in present-day smartphones, gait verification and identification can be exploited for low-level security [1]. Achieving this requires machine learning of a time sequence.\",\"PeriodicalId\":6752,\"journal\":{\"name\":\"2019 Symposium on VLSI Technology\",\"volume\":\"10 1\",\"pages\":\"T84-T85\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSIT.2019.8776571\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIT.2019.8776571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gait identification using stochastic OXRRAM-based time sequence machine learning
The way a person walks, i.e. his/her gait, can be as unique as a fingerprint. With portable accelerometers and/or gyroscopes available in present-day smartphones, gait verification and identification can be exploited for low-level security [1]. Achieving this requires machine learning of a time sequence.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
