{"title":"基于LSTM的步态预测研究","authors":"Bofan Liang, Qili Chen","doi":"10.5121/CSIT.2019.90603","DOIUrl":null,"url":null,"abstract":"With an aging population that continues to grow, the protection and assistance of the older persons has become a very important issue. Falls are the main safety problems of the elderly people, so it is very important to predict the falls. In this paper, a gait prediction method is proposed. Firstly, the lumbar posture of the human body is measured by the acceleration gyroscope as the gait feature, and then the gait is predicted by the LSTM network. The experimental results show that the RMSE between the gait trend predicted by the method and the actual gait trend can be reached a level of 0.06 ± 0.01. The proposed method can predict the gait trend well.","PeriodicalId":372948,"journal":{"name":"Computer Science & Information Technology (CS & IT )","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RESEARCH ON GAIT PREDICTION BASED ON LSTM\",\"authors\":\"Bofan Liang, Qili Chen\",\"doi\":\"10.5121/CSIT.2019.90603\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With an aging population that continues to grow, the protection and assistance of the older persons has become a very important issue. Falls are the main safety problems of the elderly people, so it is very important to predict the falls. In this paper, a gait prediction method is proposed. Firstly, the lumbar posture of the human body is measured by the acceleration gyroscope as the gait feature, and then the gait is predicted by the LSTM network. The experimental results show that the RMSE between the gait trend predicted by the method and the actual gait trend can be reached a level of 0.06 ± 0.01. The proposed method can predict the gait trend well.\",\"PeriodicalId\":372948,\"journal\":{\"name\":\"Computer Science & Information Technology (CS & IT )\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science & Information Technology (CS & IT )\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/CSIT.2019.90603\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science & Information Technology (CS & IT )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/CSIT.2019.90603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With an aging population that continues to grow, the protection and assistance of the older persons has become a very important issue. Falls are the main safety problems of the elderly people, so it is very important to predict the falls. In this paper, a gait prediction method is proposed. Firstly, the lumbar posture of the human body is measured by the acceleration gyroscope as the gait feature, and then the gait is predicted by the LSTM network. The experimental results show that the RMSE between the gait trend predicted by the method and the actual gait trend can be reached a level of 0.06 ± 0.01. The proposed method can predict the gait trend well.
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