{"title":"步长是比步行速度更可靠的行人死角测量方法。","authors":"Fatemeh Elyasi, Roberto Manduchi","doi":"10.1109/ipin57070.2023.10332483","DOIUrl":null,"url":null,"abstract":"<p><p>Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step.</p>","PeriodicalId":510887,"journal":{"name":"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752414/pdf/","citationCount":"0","resultStr":"{\"title\":\"Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning.\",\"authors\":\"Fatemeh Elyasi, Roberto Manduchi\",\"doi\":\"10.1109/ipin57070.2023.10332483\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step.</p>\",\"PeriodicalId\":510887,\"journal\":{\"name\":\"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752414/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ipin57070.2023.10332483\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ipin57070.2023.10332483","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/6 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning.
Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step.