An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors
Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step...
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
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2024
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/3499 |
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oai:scholar.dlu.edu.vn:123456789-34992024-06-05T02:05:06Z An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors Nguyễn, Hữu Khánh Dương, Bảo Ninh (Ninh Duong-Bao) Nguyễn, Thị Lương Đỗ, Thị Lệ Huỳnh, Thị Thu Thủy Lee, Seon-Woo Indoor Positioning System Heading Estimation Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step detection, step length estimation, and heading estimation. Among them, the last component is the most challenging since a small heading error accumulates to produce a very large positioning error, especially when the pedestrian holds the smartphone in unconstrained styles such as swinging the phone freely along the pedestrian’s walking direction or putting the phone into the pants’ front pockets. The research proposes an adaptive heading estimation method to deal with heading errors caused by smartphone holding styles. The novelties are described as follows. Firstly, the proposed method attempts to classify the four basic smartphone holding styles using a machine learning algorithm based on simple features of acceleration values to give pedestrians more freedom during the walking period. Secondly, the proposed method adaptively combines the two heading estimation methods, which are calculated from the integrated sensors, to determine the walking direction for different smartphone holding styles. The experimental results show that the proposed heading estimation method achieves average heading errors of less than 30 degrees when testing in two different walking paths with the smartphone held in dynamic styles. It helps to reduce the heading errors by more than 15% compared to previous heading estimation 109-121 2024-06-05T02:05:03Z 2024-06-05T02:05:03Z 2024-04-29 Journal article Bài báo đăng trên tạp chí thuộc SCOPUS, bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/3499 en CommIT Journal |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Indoor Positioning System Heading Estimation |
| spellingShingle |
Indoor Positioning System Heading Estimation Nguyễn, Hữu Khánh Dương, Bảo Ninh (Ninh Duong-Bao) Nguyễn, Thị Lương Đỗ, Thị Lệ Huỳnh, Thị Thu Thủy Lee, Seon-Woo An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| description |
Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step detection, step length estimation, and heading estimation. Among them, the last component is the most challenging since a small heading error accumulates to produce a very large positioning error, especially when the pedestrian holds the smartphone in unconstrained styles such as swinging the phone freely along the pedestrian’s walking direction or putting the phone into the pants’ front pockets. The research proposes an adaptive heading estimation method to deal with heading errors caused by smartphone holding styles. The novelties are described as follows. Firstly, the proposed method attempts to classify the four basic smartphone holding styles using a machine learning algorithm based on simple features of acceleration values to give pedestrians more freedom during the walking period. Secondly, the proposed method adaptively combines the two heading estimation methods, which are calculated from the integrated sensors, to determine the walking direction for different smartphone holding styles. The experimental results show that the proposed heading estimation method achieves average heading errors of less than 30 degrees when testing in two different walking paths with the smartphone held in dynamic styles. It helps to reduce the heading errors by more than 15% compared to previous heading estimation |
| format |
Journal article |
| author |
Nguyễn, Hữu Khánh Dương, Bảo Ninh (Ninh Duong-Bao) Nguyễn, Thị Lương Đỗ, Thị Lệ Huỳnh, Thị Thu Thủy Lee, Seon-Woo |
| author_facet |
Nguyễn, Hữu Khánh Dương, Bảo Ninh (Ninh Duong-Bao) Nguyễn, Thị Lương Đỗ, Thị Lệ Huỳnh, Thị Thu Thủy Lee, Seon-Woo |
| author_sort |
Nguyễn, Hữu Khánh |
| title |
An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| title_short |
An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| title_full |
An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| title_fullStr |
An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| title_full_unstemmed |
An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors |
| title_sort |
adaptive heading estimation method based on holding styles recognition using smartphone sensors |
| publishDate |
2024 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/3499 |
| _version_ |
1813142615666720768 |