Fall Detection and Direction Judgment Based on Posture Estimation
For the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, w...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/8372291 |
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| _version_ | 1832562178503213056 |
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| author | Chunmiao Yuan Pengju Zhang Qingyong Yang Jianming Wang |
| author_facet | Chunmiao Yuan Pengju Zhang Qingyong Yang Jianming Wang |
| author_sort | Chunmiao Yuan |
| collection | DOAJ |
| description | For the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, which will bring inconvenience to their lives and few algorithms pay attention to the direction of the fall. Therefore, we propose a video-based fall detection and direction judgment method based on human posture estimation for the first time. We predict the joint point coordinates of each human body through the posture estimation network, and then use the SVM classifier to detect falls. Next, we will use the three-dimensional human posture information to judge the direction of the fall. Compared to the existing methods, our method has a great improvement in sensitivity, specificity, and accuracy which reaches 95.86, 99.5, and 97.52 on the Le2i fall dataset, respectively, whereas on the UR fall dataset, they are 95.45, 100, and 97.43, respectively. |
| format | Article |
| id | doaj-art-434e146d75854256b2928797e6fc98f3 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-434e146d75854256b2928797e6fc98f32025-02-03T01:23:10ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/8372291Fall Detection and Direction Judgment Based on Posture EstimationChunmiao Yuan0Pengju Zhang1Qingyong Yang2Jianming Wang3School of SoftwareSchool of Computer Science and TechnologySchool of Software and CommunicationSchool of Computer Science and TechnologyFor the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, which will bring inconvenience to their lives and few algorithms pay attention to the direction of the fall. Therefore, we propose a video-based fall detection and direction judgment method based on human posture estimation for the first time. We predict the joint point coordinates of each human body through the posture estimation network, and then use the SVM classifier to detect falls. Next, we will use the three-dimensional human posture information to judge the direction of the fall. Compared to the existing methods, our method has a great improvement in sensitivity, specificity, and accuracy which reaches 95.86, 99.5, and 97.52 on the Le2i fall dataset, respectively, whereas on the UR fall dataset, they are 95.45, 100, and 97.43, respectively.http://dx.doi.org/10.1155/2022/8372291 |
| spellingShingle | Chunmiao Yuan Pengju Zhang Qingyong Yang Jianming Wang Fall Detection and Direction Judgment Based on Posture Estimation Discrete Dynamics in Nature and Society |
| title | Fall Detection and Direction Judgment Based on Posture Estimation |
| title_full | Fall Detection and Direction Judgment Based on Posture Estimation |
| title_fullStr | Fall Detection and Direction Judgment Based on Posture Estimation |
| title_full_unstemmed | Fall Detection and Direction Judgment Based on Posture Estimation |
| title_short | Fall Detection and Direction Judgment Based on Posture Estimation |
| title_sort | fall detection and direction judgment based on posture estimation |
| url | http://dx.doi.org/10.1155/2022/8372291 |
| work_keys_str_mv | AT chunmiaoyuan falldetectionanddirectionjudgmentbasedonpostureestimation AT pengjuzhang falldetectionanddirectionjudgmentbasedonpostureestimation AT qingyongyang falldetectionanddirectionjudgmentbasedonpostureestimation AT jianmingwang falldetectionanddirectionjudgmentbasedonpostureestimation |