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Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation
This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156317/ https://www.ncbi.nlm.nih.gov/pubmed/34069129 http://dx.doi.org/10.3390/s21103428 |
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author | Dang, Duc Cong Suh, Young Soo |
author_facet | Dang, Duc Cong Suh, Young Soo |
author_sort | Dang, Duc Cong |
collection | PubMed |
description | This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources. |
format | Online Article Text |
id | pubmed-8156317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81563172021-05-28 Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation Dang, Duc Cong Suh, Young Soo Sensors (Basel) Communication This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources. MDPI 2021-05-14 /pmc/articles/PMC8156317/ /pubmed/34069129 http://dx.doi.org/10.3390/s21103428 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Dang, Duc Cong Suh, Young Soo Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title | Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title_full | Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title_fullStr | Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title_full_unstemmed | Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title_short | Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation |
title_sort | improved single inertial-sensor-based attitude estimation during walking using velocity-aided observation |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156317/ https://www.ncbi.nlm.nih.gov/pubmed/34069129 http://dx.doi.org/10.3390/s21103428 |
work_keys_str_mv | AT dangduccong improvedsingleinertialsensorbasedattitudeestimationduringwalkingusingvelocityaidedobservation AT suhyoungsoo improvedsingleinertialsensorbasedattitudeestimationduringwalkingusingvelocityaidedobservation |