基于频域特征分析和步态识别的步长估算方法,用于行人惯性计算

IF 1.6 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION
Guosheng Deng, Wei Zhang, Zhitao Wu, Minglei Guan, Dejin Zhang
{"title":"基于频域特征分析和步态识别的步长估算方法,用于行人惯性计算","authors":"Guosheng Deng, Wei Zhang, Zhitao Wu, Minglei Guan, Dejin Zhang","doi":"10.1108/sr-05-2024-0484","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.</p><!--/ Abstract__block -->","PeriodicalId":49540,"journal":{"name":"Sensor Review","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning\",\"authors\":\"Guosheng Deng, Wei Zhang, Zhitao Wu, Minglei Guan, Dejin Zhang\",\"doi\":\"10.1108/sr-05-2024-0484\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.</p><!--/ Abstract__block -->\",\"PeriodicalId\":49540,\"journal\":{\"name\":\"Sensor Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sensor Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/sr-05-2024-0484\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensor Review","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/sr-05-2024-0484","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

摘要

目的步长是行人惯性导航(PDR)的关键因素,影响定位精度和可靠性。传统方法难以处理动态步态的步长估计,误差较大,不适应真实行走。本文旨在提出一种基于频域特征分析和步态识别的步长估算方法,用于 PDR,该方法考虑了实时步态的影响。设计/方法/途径新的步长估算方法将行人的加速度从时域转换到频域,获得了行人的步态特征,并与不同的步行速度相匹配。原创性/价值提出了在快速傅立叶变换中使用滑动窗口策略来实现加速度的频域分析,并提出了一种快速自适应步态识别机制来识别行人的步态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

Purpose

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

Design/methodology/approach

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Findings

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

Originality/value

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Sensor Review
Sensor Review 工程技术-仪器仪表
CiteScore
3.40
自引率
6.20%
发文量
50
审稿时长
3.7 months
期刊介绍: Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments. Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles. All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable. Sensor Review’s coverage includes, but is not restricted to: Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors Temperature sensors, infrared sensors, humidity sensors Optical, electro-optical and fibre-optic sensors and systems, photonic sensors Biosensors, wearable and implantable sensors and systems, immunosensors Gas and chemical sensors and systems, polymer sensors Acoustic and ultrasonic sensors Haptic sensors and devices Smart and intelligent sensors and systems Nanosensors, NEMS, MEMS, and BioMEMS Quantum sensors Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信