A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar
Abstract Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing betwee...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-06-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-021-00738-2 |
| _version_ | 1818670993730699264 |
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| author | Yangyang Ma Pengfei Wang Wenzhe Huang Fugui Qi Fulai Liang Hao Lv Xiao Yu Jianqi Wang Yang Zhang |
| author_facet | Yangyang Ma Pengfei Wang Wenzhe Huang Fugui Qi Fulai Liang Hao Lv Xiao Yu Jianqi Wang Yang Zhang |
| author_sort | Yangyang Ma |
| collection | DOAJ |
| description | Abstract Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring. |
| first_indexed | 2024-12-17T07:16:56Z |
| format | Article |
| id | doaj.art-27d3c7e3f2344137b845d62d20c324ef |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-17T07:16:56Z |
| publishDate | 2021-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-27d3c7e3f2344137b845d62d20c324ef2022-12-21T21:58:51ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802021-06-012021112410.1186/s13634-021-00738-2A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radarYangyang Ma0Pengfei Wang1Wenzhe Huang2Fugui Qi3Fulai Liang4Hao Lv5Xiao Yu6Jianqi Wang7Yang Zhang8Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Biomedical Engineering, Jinling Hospital, Clinical School of Medicine, Nanjing UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityDepartment of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical UniversityAbstract Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.https://doi.org/10.1186/s13634-021-00738-2Distinguishing between humans and petsUWB radarNon-contact vital signs monitoringSupport vector machine (SVM) |
| spellingShingle | Yangyang Ma Pengfei Wang Wenzhe Huang Fugui Qi Fulai Liang Hao Lv Xiao Yu Jianqi Wang Yang Zhang A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar EURASIP Journal on Advances in Signal Processing Distinguishing between humans and pets UWB radar Non-contact vital signs monitoring Support vector machine (SVM) |
| title | A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar |
| title_full | A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar |
| title_fullStr | A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar |
| title_full_unstemmed | A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar |
| title_short | A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar |
| title_sort | robust multi feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using uwb radar |
| topic | Distinguishing between humans and pets UWB radar Non-contact vital signs monitoring Support vector machine (SVM) |
| url | https://doi.org/10.1186/s13634-021-00738-2 |
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