Method of bed exit intention based on the internal pressure features in array air spring mattress
With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring ma...
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Published in | Scientific reports Vol. 14; no. 1; pp. 27273 - 15 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Nature Publishing Group UK
08.11.2024
Nature Publishing Group Nature Portfolio |
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Abstract | With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment. |
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AbstractList | With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment. Abstract With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment. With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment. |
ArticleNumber | 27273 |
Author | Meng, Fanchao Liu, Teng Zhang, Yifan Guo, Shijie Zhang, Jianjun Meng, Chuizhou |
Author_xml | – sequence: 1 givenname: Fanchao surname: Meng fullname: Meng, Fanchao organization: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Hebei Key Laboratory of Smart Sensing and Human-Robot Integration, Hebei University of Technology, School of Mechanical Engineering, Hebei University of Technology – sequence: 2 givenname: Teng surname: Liu fullname: Liu, Teng email: wuqiu-liu@163.com organization: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Hebei Key Laboratory of Smart Sensing and Human-Robot Integration, Hebei University of Technology, School of Mechanical Engineering, Hebei University of Technology – sequence: 3 givenname: Chuizhou surname: Meng fullname: Meng, Chuizhou organization: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Hebei Key Laboratory of Smart Sensing and Human-Robot Integration, Hebei University of Technology, School of Mechanical Engineering, Hebei University of Technology – sequence: 4 givenname: Jianjun surname: Zhang fullname: Zhang, Jianjun organization: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Hebei Key Laboratory of Smart Sensing and Human-Robot Integration, Hebei University of Technology, School of Mechanical Engineering, Hebei University of Technology – sequence: 5 givenname: Yifan surname: Zhang fullname: Zhang, Yifan organization: School of Computer Science and Technology, Civil Aviation University of China – sequence: 6 givenname: Shijie surname: Guo fullname: Guo, Shijie organization: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Hebei Key Laboratory of Smart Sensing and Human-Robot Integration, Hebei University of Technology, School of Mechanical Engineering, Hebei University of Technology |
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Keywords | Global features Array air spring mattress Local features Bed-exit intention Internal pressure |
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SubjectTerms | 639/166 692/700 Array air spring mattress Bed-exit intention Bedding Global features Humanities and Social Sciences Internal pressure Local features multidisciplinary Neural networks Science Science (multidisciplinary) Spatial distribution |
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Title | Method of bed exit intention based on the internal pressure features in array air spring mattress |
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