Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm Article Swipe
Haifeng Xing
,
Jinglong Li
,
Bo Hou
,
Yongjian Zhang
,
Meifeng Guo
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/6091261
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/6091261
Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.
Related Topics
Concepts
STRIDE
Inertial measurement unit
Step detection
Algorithm
Dead reckoning
Nonlinear system
Computer science
Artificial neural network
Inertial frame of reference
Pedestrian
Artificial intelligence
Engineering
Global Positioning System
Telecommunications
Physics
Computer security
Transport engineering
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2017/6091261
- http://downloads.hindawi.com/journals/js/2017/6091261.pdf
- OA Status
- hybrid
- Cited By
- 72
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2586574952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2586574952Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2017/6091261Digital Object Identifier
- Title
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Pedestrian Stride Length Estimation from IMU Measurements and ANN Based AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-01-01Full publication date if available
- Authors
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Haifeng Xing, Jinglong Li, Bo Hou, Yongjian Zhang, Meifeng GuoList of authors in order
- Landing page
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https://doi.org/10.1155/2017/6091261Publisher landing page
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https://downloads.hindawi.com/journals/js/2017/6091261.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/js/2017/6091261.pdfDirect OA link when available
- Concepts
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STRIDE, Inertial measurement unit, Step detection, Algorithm, Dead reckoning, Nonlinear system, Computer science, Artificial neural network, Inertial frame of reference, Pedestrian, Artificial intelligence, Engineering, Global Positioning System, Telecommunications, Physics, Computer security, Transport engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
72Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 5, 2023: 12, 2022: 12, 2021: 15Per-year citation counts (last 5 years)
- References (count)
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12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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