Falling motion detection algorithm based on deep learning
Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices,...
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| Published in | IET image processing Vol. 16; no. 11; pp. 2845 - 2853 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.09.2022
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| Online Access | Get full text |
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| Abstract | Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices, which need to be worn all the time, and the cost of the device is high. In recent years, the fall detection method based on computer vision has become a research hot spot. This paper proposes a framework for falling motion detection based on deep learning. To quickly and accurately classify human movements, a method using bone key points as the feature descriptors of human movements is proposed. The OpenPose algorithm is used to extract the human skeleton point information as the primary human body feature, and then use the deep learning method to classify further and recognise our action features. In this paper, four types of daily actions, such as falling and walking, are classified and recognised. The results show that the algorithm achieves an accuracy of 99.4% on our dataset. Simultaneously, 86.1% accuracy is reached in the public dataset fall detection dataset. |
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| AbstractList | Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices, which need to be worn all the time, and the cost of the device is high. In recent years, the fall detection method based on computer vision has become a research hot spot. This paper proposes a framework for falling motion detection based on deep learning. To quickly and accurately classify human movements, a method using bone key points as the feature descriptors of human movements is proposed. The OpenPose algorithm is used to extract the human skeleton point information as the primary human body feature, and then use the deep learning method to classify further and recognise our action features. In this paper, four types of daily actions, such as falling and walking, are classified and recognised. The results show that the algorithm achieves an accuracy of 99.4% on our dataset. Simultaneously, 86.1% accuracy is reached in the public dataset fall detection dataset. Abstract Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices, which need to be worn all the time, and the cost of the device is high. In recent years, the fall detection method based on computer vision has become a research hot spot. This paper proposes a framework for falling motion detection based on deep learning. To quickly and accurately classify human movements, a method using bone key points as the feature descriptors of human movements is proposed. The OpenPose algorithm is used to extract the human skeleton point information as the primary human body feature, and then use the deep learning method to classify further and recognise our action features. In this paper, four types of daily actions, such as falling and walking, are classified and recognised. The results show that the algorithm achieves an accuracy of 99.4% on our dataset. Simultaneously, 86.1% accuracy is reached in the public dataset fall detection dataset. |
| Author | Zhu, Na Zhao, Guangzhe Zhang, Xiaolong Jin, Zhexue |
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| Cites_doi | 10.1109/ACCESS.2018.2861331 10.1016/j.physa.2019.123205 10.1007/978-3-030-38748-8_5 10.1109/ICIEA.2014.6931308 10.1109/MPRV.2016.27 10.5815/ijigsp.2015.12.05 10.1109/MMUL.2012.24 10.1007/978-3-319-46448-0_2 10.3758/BF03212378 10.1007/978-981-10-8663-2_23 10.1007/s11760-019-01490-9 10.1109/TPAMI.2019.2929257 10.1109/AVSS.2014.6918647 10.1007/978-981-32-9291-8_33 10.3390/s17092096 10.3390/s18041101 10.23919/MVA.2017.7986795 10.23919/ChiCC.2019.8865732 10.1177/1847979018787905 10.1371/journal.pone.0124068 10.1016/j.procs.2019.01.264 10.1016/j.cviu.2015.10.013 10.1109/TFUZZ.2016.2514366 10.1109/CVPR.2016.91 |
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| Title | Falling motion detection algorithm based on deep learning |
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