EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone
The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophistica...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6864 |
| _version_ | 1797482614106357760 |
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| author | Mingcong Shu Guoliang Chen Zhenghua Zhang |
| author_facet | Mingcong Shu Guoliang Chen Zhenghua Zhang |
| author_sort | Mingcong Shu |
| collection | DOAJ |
| description | The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated gaits or heterogeneous devices, their inaccuracy varies dramatically. This paper proposes an efficient learning-based stride-length estimation model using a smartphone to obtain the correct stride length. The model uses adaptive learning to extract different elements for changing and recognition tasks, including Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules. The direct fusion method maps the eigenvectors to the appropriate stride length after combining the features from the learning modules. We presented an online learning module to update the model to increase the SLE model’s generalization. Extensive experiments are conducted with heterogeneous devices or users, various gaits, and switched scenarios. The results confirm that the proposed method outperforms other state-of-the-art methods and achieves an average 4.26% estimation error rate in various environments. |
| first_indexed | 2024-03-09T22:34:56Z |
| format | Article |
| id | doaj.art-8a2f6c9d145a48ea862ba99a131e90da |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:34:56Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a2f6c9d145a48ea862ba99a131e90da2023-11-23T18:50:32ZengMDPI AGSensors1424-82202022-09-012218686410.3390/s22186864EL-SLE: Efficient Learning Based Stride-Length Estimation Using a SmartphoneMingcong Shu0Guoliang Chen1Zhenghua Zhang2School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 21116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 21116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 21116, ChinaThe pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated gaits or heterogeneous devices, their inaccuracy varies dramatically. This paper proposes an efficient learning-based stride-length estimation model using a smartphone to obtain the correct stride length. The model uses adaptive learning to extract different elements for changing and recognition tasks, including Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules. The direct fusion method maps the eigenvectors to the appropriate stride length after combining the features from the learning modules. We presented an online learning module to update the model to increase the SLE model’s generalization. Extensive experiments are conducted with heterogeneous devices or users, various gaits, and switched scenarios. The results confirm that the proposed method outperforms other state-of-the-art methods and achieves an average 4.26% estimation error rate in various environments.https://www.mdpi.com/1424-8220/22/18/6864indoor positioningstride-length estimationCNNLSTMadaptive learningsmartphone sensors |
| spellingShingle | Mingcong Shu Guoliang Chen Zhenghua Zhang EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone Sensors indoor positioning stride-length estimation CNN LSTM adaptive learning smartphone sensors |
| title | EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone |
| title_full | EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone |
| title_fullStr | EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone |
| title_full_unstemmed | EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone |
| title_short | EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone |
| title_sort | el sle efficient learning based stride length estimation using a smartphone |
| topic | indoor positioning stride-length estimation CNN LSTM adaptive learning smartphone sensors |
| url | https://www.mdpi.com/1424-8220/22/18/6864 |
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