A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost
Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comp...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8736849/ |
| _version_ | 1818855377358290944 |
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| author | Wenting Zhang Xiaohui Zhao Zan Li |
| author_facet | Wenting Zhang Xiaohui Zhao Zan Li |
| author_sort | Wenting Zhang |
| collection | DOAJ |
| description | Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comprehensive study for recognizing indoor activities related to multi-floor indoor positioning based on a robust machine learning algorithm. In this work, we propose a framework for recognizing five indoor activities, i.e., walking, stillness, stair climbing, escalator, or elevator taking. In this framework, we investigate the relevant sensors and features to improve the recognition accuracy of these activities, especially some specific features in the frequency domain and wavelet domain. We propose to utilize a promising tree-based ensemble learning classifier, XGBoost, to recognize these activities. Based on our dataset created by 40 volunteers, we provide a comprehensive analysis of the proposed framework for indoor activity recognition. Considering both accuracy and computational cost, the XGBoost-based indoor activity recognition algorithm outperforms the other ensemble learning classifiers and single classifiers, and the average recognition F-score of XGBoost reaches 84.41%. In addition, our introduced specific features in the frequency domain and wavelet domain can significantly improve the recognition accuracy. Moreover, we use a publicly available dataset to verify our proposed framework and XGBoost classifier reaches 84.19% that outperforms the other classifiers. |
| first_indexed | 2024-12-19T08:07:38Z |
| format | Article |
| id | doaj.art-29a16d856d8d4ea9bcffc00e4fe9c46b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:07:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29a16d856d8d4ea9bcffc00e4fe9c46b2022-12-21T20:29:43ZengIEEEIEEE Access2169-35362019-01-017800278004210.1109/ACCESS.2019.29229748736849A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via XgboostWenting Zhang0Xiaohui Zhao1https://orcid.org/0000-0001-6531-5204Zan Li2https://orcid.org/0000-0002-0045-2010College of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaRecently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comprehensive study for recognizing indoor activities related to multi-floor indoor positioning based on a robust machine learning algorithm. In this work, we propose a framework for recognizing five indoor activities, i.e., walking, stillness, stair climbing, escalator, or elevator taking. In this framework, we investigate the relevant sensors and features to improve the recognition accuracy of these activities, especially some specific features in the frequency domain and wavelet domain. We propose to utilize a promising tree-based ensemble learning classifier, XGBoost, to recognize these activities. Based on our dataset created by 40 volunteers, we provide a comprehensive analysis of the proposed framework for indoor activity recognition. Considering both accuracy and computational cost, the XGBoost-based indoor activity recognition algorithm outperforms the other ensemble learning classifiers and single classifiers, and the average recognition F-score of XGBoost reaches 84.41%. In addition, our introduced specific features in the frequency domain and wavelet domain can significantly improve the recognition accuracy. Moreover, we use a publicly available dataset to verify our proposed framework and XGBoost classifier reaches 84.19% that outperforms the other classifiers.https://ieeexplore.ieee.org/document/8736849/Activity recognitionensemble learningXGBoostsmartphone |
| spellingShingle | Wenting Zhang Xiaohui Zhao Zan Li A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost IEEE Access Activity recognition ensemble learning XGBoost smartphone |
| title | A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost |
| title_full | A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost |
| title_fullStr | A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost |
| title_full_unstemmed | A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost |
| title_short | A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost |
| title_sort | comprehensive study of smartphone based indoor activity recognition via xgboost |
| topic | Activity recognition ensemble learning XGBoost smartphone |
| url | https://ieeexplore.ieee.org/document/8736849/ |
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