# Design of Intelligent Nursing System Based on Artificial Intelligence

Authors: Suzhen Wu
Journal: Journal of Sensors
 (2022)

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## Abstract

As the number of the elderly population and the population dependency ratio increase year by year, the issue of old-age care has become the focus. However, due to the shortage of carers and the large-scale and expensive auxiliary equipment for the elderly, it is difficult to give thoughtful care to all the elderly. In view of the above background, this paper designs a set of intelligent nursing system for the elderly based on artificial intelligence (AI) algorithms, which mainly includes sensor terminals and AI processing algorithms. Among them, the sensor terminal mainly includes two parts: video monitoring and human biological signal monitoring. For video surveillance signals, this paper uses scene event detection algorithm to detect abnormal events, so as to automatically perceive possible unexpected situations. For human biological signals, such as heart rate, blood pressure, and pulse, the abnormal detection is carried out through data analysis and comparison with the normal index range. So, the possible problems in the physical state of the elderly can be judged in time. Through more comprehensive state monitoring and reliable algorithm processing, the system can effectively solve many hidden dangers in the current elderly care and provide a feasible solution for smart nursing.


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## Body

## 1. Introduction

With the acceleration of aging in China and the increasingly complex spectrum of modern diseases, the contradiction between the increasing demand for health and the lack of medical and health resources is becoming more and more prominent [1–3]. Nurses, as the main group of people who perform treatment and preventive rehabilitation in the medical industry, play an important role in the whole process of hospital care. However, the shortage of clinical nurses has been serious for a long time. How to provide patients with high-quality and high-efficiency whole-process nursing under the condition of shortage of nurses’ human resources is an urgent problem to be solved by hospital nursing managers. From the current situation, nurses often have to take care of multiple elderly people by one person [4–6]. The one-to-many care method makes the nursing staff unable to give comprehensive care to each elderly person, and it is easy to cause serious consequences such as the elderly taking wrong medicines, physical abnormalities, or accidental loss. When the nursing job is replaced, the precautions can only be simply communicated orally or in writing, which is prone to mistakes. Therefore, it is particularly important to design a smart nursing system for nursing homes that can meet the management needs of nursing homes and improve the monitoring level and management efficiency.The current nursing home informatization system mostly focuses on the business management level and cannot solve the hidden dangers in the nursing process of the elderly. At the same time, due to the lack of human resources, there are many deficiencies in the pertinence and prevention of elderly care work. With the rapid development of artificial intelligence (AI) technology, it has played a huge role in all walks of life. In the medical and health field, AI technology has played a great role in disease diagnosis, medical image processing, biomedical signal processing, and even drug research and development. In recent years, with the development of smart medical care and medical Internet of Things, many scholars have successively proposed smart nursing system design ideas for different nursing groups, aiming to improve the automation level of nursing systems. In this context, this paper proposes a design method of an intelligent nursing system for the elderly based on AI algorithms. This method is based on the intelligent anomaly detection algorithm and carries out targeted nursing work for the elderly by combining the two characteristics of surveillance video and biological signals. Video anomaly detection solves the problem of finding a small number of abnormal events in large scenes, mainly for the fall and bumps that may occur in the elderly. As one of the hot issues in the field of machine vision, video anomaly detection has been extensively studied, and existing methods can be classified into two categories. One is the method using handcrafted features. Such methods often use artificial features to represent events, such as object trajectory features [7–10], spatiotemporal gradients [11–13], optical flow histograms [14–17], and dynamic blending textures [18–21]. After obtaining the features representing events in this type of method, the next step is to use common machine learning models such as support vector machines (SVM) and Gaussian process models to build anomaly detection models. The second category is based on deep learning methods. Since handcrafted features are difficult to adapt to various abnormal events, researchers replace handcrafted features by using raw video frames as the input of the model through deep learning algorithms. Typical representatives of such methods are autoencoders, convolutional neural networks (CNN), long-short term memory (LSTM), generative adversarial networks (GAN), etc. [22–25]. Based on the existing researches, this paper mainly designs a video anomaly detection algorithm based on the autoencoder framework to detect the abnormal behavior of the elderly in large scenes. The method uses the improved LSTM network model to build an encoder and a decoder, which are used to learn the spatial feature representation of the video sequence and reconstruct the video sequence, respectively. Then, it performs adaptive abnormal event detection based on the reconstruction error. In terms of abnormal detection of human biological signals, data such as heart rate, blood pressure, and pulse of the elderly are collected through sensors. Based on the video anomaly detection in the previous stage, the current relevant parameters of the abnormal behavior person are compared with the data in the normal state [26, 27]. When it is found that the current measurement parameters exceed the normal range, it can be judged that the abnormal behavior person has a greater risk and prompt rescue services should be taken at this time. Depending on the severity of such situations, different levels of treatment and nursing measures can be targeted. In summary, this paper introduces AI technology and uses anomaly detection algorithm to automatically detect potential threats of the elderly, so as to provide targeted nursing and treatment services and reduce the pressure on nursing staff.


## 2. System Framework

The overall framework of the smart nursing system for the elderly based on AI technology is shown in Figure1, which mainly includes two parts: sensor module and AI data processing module. The sensor module contains public surveillance video in the hospital and biomedical signal measurement equipment worn by the elderly. Video sensors mainly acquire moving images of the elderly, based on which abnormal behavior detection can be carried out to determine the elderly who may have abnormal behaviors. On the basis of video anomaly detection and positioning, the system determines the health status of the elderly with abnormal behavior according to the biomedical signal characteristics returned by the wearable detection device, such as heart rate, blood pressure, pulse, and other parameters, and then the targeted care measure can be token.Figure 1
Basic flowchart of the proposed method.According to the AI algorithm selected in this paper, the system workflow can be summarized into the following key steps:Step 1.
Based on the real-time surveillance video, the video anomaly detection algorithm based on the autoencoder is employed to locate the abnormal events in the video and find the people with abnormal behaviors.Step 2.
For the abnormal behavior person, the biomedical signal data is obtained, and the system will judge the health status based on the historical health data.Step 3.
Based on the judgment of the abnormal behavior person’s health status, the system will remind the nursing staff provide targeted rescue or nursing measures.It can be seen from the above process that the nursing system based on AI algorithm is automatic and targeted and can achieve effective monitoring of the elderly in large scenes at a small labor cost. Accordingly, the system can assist in carrying out targeted nursing work and has strong practical operability.


## 3. Method Description

### 3.1. Video Anomaly Detection

#### 3.1.1. Modified LSTM

Recurrent neural network (RNN) works like a feedforward network, except that the value of its input vector is affected not only by the input vector but also by the entire input history. In theory, RNN can use information in arbitrarily long sequences. But in practical implementation, they can only go back a few steps due to vanishing gradients. To overcome this problem, a variant of RNN, the LSTM model, is introduced, as shown in Figure2. Using this new structure, LSTM prevents the error diffusion and explosion of backpropagation so long sequences can be processed. And they can be stacked together to capture higher-order information.Figure 2
Internal structure of LSTM.In the figure,c is the cell state, which acts like an information pipeline and runs through the entire operation cycle of LSTM. The three gate structures of LSTM can delete and add information in cells, so that information can flow selectively. σ is a nonlinear activation function, which maps the output value of the function between 0 and 1, where 0 means no information passes and1 means all information passes.The convolutional LSTM (ConvLSTM) model is an improved form of the LSTM architecture, and the matrix operation is replaced by convolution compared to the general LSTM. By using convolutions for input-to-hidden and hidden-to-hidden connections, ConvLSTM requires fewer weights and produces better spatial feature maps. The formula of the ConvLSTM unit can be summarized as follows:(1)ft=σWXF∗Xt+WHF∗ht−1+WCF∘Ct−1+bF,where ft is the output value of the forget gate, which decides to delete or forget part of the information from the storage unit; ht−1 is the output value at the previous moment; Xt is the current input value; W and b are the matrix of coefficients and the vector of bias, respectively; and σ is the sigmoid activation function.
(2)it=σWXI∗Xt+WHI∗ht−1+WCI∘Ct−1+bI.Equation (2) is used to calculate the value of the input gate it to determine how much information to update, and the output is a value between 0 and 1.
(3)Ct=ft∘Ct−1+it·tanhWXC∗Xt+WHC∗ht−1+bC.Equation (3) is used to update the cell state. The old cell state Ct−1 is multiplied by the output value of the forget gate ft, the output updated postselection value expression is multiplied by the input gate it, and the two are added to obtain the new cell state Ct.
(4)Ot=σWXO∗Xt+WHO∗ht−1+WCO∘Ct+bo.Equation (4) is used to calculate the value of the output gate Ot, which determines how much memory is used for the output.
(5)ht=Ot∘tanhCt.The final step is to use the hyperbolic tangent function to update the valueCt to be between -1 and 1 and multiply the output gate value Ot with it to get the final output value ht at time t.In the above equations,∗ represents the convolution operation; ∘ represents the Hadamard product; Xt represents the input image at the moment t; it represents the input gate output information at the moment it; Ct−1 represents the information of the memory unit at the moment t−1; and WHI is the weight matrix from the input gate to the forget gate and so on for the rest of the matrices.


#### 3.1.2. Detection Algorithm

The basic principle of anomaly detection method can be described as follows. When an anomaly event occurs, the current video frame will be significantly different from the previous video frame. Accordingly, an end-to-end model is trained, which consists of a spatial feature extractor and a temporal encoder-decoder that jointly learn the temporal patterns of the input video sequence. The model is only trained on videos consisting of normal scenes, with the goal of minimizing the reconstruction error between the input and output videos reconstructed by the learned model. After proper training of the model, normal videos are expected to have low reconstruction errors, while videos containing abnormal scenes are expected to have high reconstruction errors. Anomalies are adaptively detected by thresholding the error produced by each test input video.(1) Autoencoder. Autoencoder learns regular patterns in training videos based on the autoencoder framework, which consists of two parts, namely, a spatial autoencoder for learning the spatial structure of each video frame and a temporal encoding for learning the temporal structure. The spatial encoder and decoder consist of two convolutional and deconvolutional layers, respectively, while the temporal encoder-decoder consists of a three-layer ConvLSTM model. It takes a sequence input of length T and outputs the reconstructed input sequence. The rightmost number represents the output size of each layer. The spatial encoder takes one frame as input at a time, and after processing T=4 frames, the encoding features of the T frames are concatenated into temporal encoding for motion encoding. The decoder mirrors the encoder to reconstruct the input video volume. An autoencoder consists of two parts: encoding and decoding. It reduces dimensionality by setting the number of encoder output units smaller than the input and is trained with an unsupervised backpropagation method to minimize the reconstruction error of the decoding results.The main purpose of the convolutions in convolutional networks is to extract features from an input image. The convolutional network will automatically learn the value of the filter during the training process, but parameters such as the number, size, and number of layers of the filter need to be specified before training. As the number of filters increases, more image features can be extracted, and the recognition performance of the network becomes much better. But more filters will increase computation time and run out of memory faster, and applications need reasonable settings to achieve a balance between accuracy and speed.(2) Reconstruction Error Calculation. The trained model can be used to obtain reconstructions of the input video sequence. The reconstruction error is represented by
(6)e=1np∑k=1n∑i=1pθ∧ki−θki2,where θ^ki is the output pixel value; θki is the input pixel value; p is the total number of pixels per frame; and n is the number of frames.A regularity score for the video can be calculated based on the error value. The regularity score normalizes the reconstruction error for each video sequence between 0 and 1. The regularity score of a sequence is given by(7)gx=1−ex−minxexmaxxex,where x is the output reconstruction sequence and ex is the reconstruction error of the sequence. Video sequences containing normal events have high regularity scores because they are similar to the data used to train the model, while sequences containing abnormal events have lower regularity scores. Accordingly, abnormal events found in the video can be detected, and corresponding results can be obtained.


### 3.2. Biomedical Signal Abnormal Judgment Method

On the basis of anomaly detection in surveillance video, the system will automatically determine the identity of the perpetrator and then associate the person’s health database. Through the health detection sensors carried by the elderly, biomedical signal characteristics such as heart rate, blood pressure, and pulse of abnormal behavior can be obtained. According to its health database, it can be determined whether the current heart rate, blood pressure, pulse, and other parameters are within its allowable range, and the physical state of the abnormal behavior person can be determined accordingly. Taking blood pressure parameters as an example, the normal range is [P1, P2] based on the analysis of the abnormal behavior health database. When the blood pressure value returned at this time exceeds the above range, it is considered that there is a risk in the physical state of the person and measures need to be taken in time. At the same time, different levels of risk, such as low risk, medium risk, and high risk, can be set according to the size of the out of range, and different solutions can be adopted accordingly. For other biomedical signal parameters, corresponding methods can be used to judge, and the final fusion decision is the corresponding risk level.


## 3.1. Video Anomaly Detection

### 3.1.1. Modified LSTM

Recurrent neural network (RNN) works like a feedforward network, except that the value of its input vector is affected not only by the input vector but also by the entire input history. In theory, RNN can use information in arbitrarily long sequences. But in practical implementation, they can only go back a few steps due to vanishing gradients. To overcome this problem, a variant of RNN, the LSTM model, is introduced, as shown in Figure2. Using this new structure, LSTM prevents the error diffusion and explosion of backpropagation so long sequences can be processed. And they can be stacked together to capture higher-order information.Figure 2
Internal structure of LSTM.In the figure,c is the cell state, which acts like an information pipeline and runs through the entire operation cycle of LSTM. The three gate structures of LSTM can delete and add information in cells, so that information can flow selectively. σ is a nonlinear activation function, which maps the output value of the function between 0 and 1, where 0 means no information passes and1 means all information passes.The convolutional LSTM (ConvLSTM) model is an improved form of the LSTM architecture, and the matrix operation is replaced by convolution compared to the general LSTM. By using convolutions for input-to-hidden and hidden-to-hidden connections, ConvLSTM requires fewer weights and produces better spatial feature maps. The formula of the ConvLSTM unit can be summarized as follows:(1)ft=σWXF∗Xt+WHF∗ht−1+WCF∘Ct−1+bF,where ft is the output value of the forget gate, which decides to delete or forget part of the information from the storage unit; ht−1 is the output value at the previous moment; Xt is the current input value; W and b are the matrix of coefficients and the vector of bias, respectively; and σ is the sigmoid activation function.
(2)it=σWXI∗Xt+WHI∗ht−1+WCI∘Ct−1+bI.Equation (2) is used to calculate the value of the input gate it to determine how much information to update, and the output is a value between 0 and 1.
(3)Ct=ft∘Ct−1+it·tanhWXC∗Xt+WHC∗ht−1+bC.Equation (3) is used to update the cell state. The old cell state Ct−1 is multiplied by the output value of the forget gate ft, the output updated postselection value expression is multiplied by the input gate it, and the two are added to obtain the new cell state Ct.
(4)Ot=σWXO∗Xt+WHO∗ht−1+WCO∘Ct+bo.Equation (4) is used to calculate the value of the output gate Ot, which determines how much memory is used for the output.
(5)ht=Ot∘tanhCt.The final step is to use the hyperbolic tangent function to update the valueCt to be between -1 and 1 and multiply the output gate value Ot with it to get the final output value ht at time t.In the above equations,∗ represents the convolution operation; ∘ represents the Hadamard product; Xt represents the input image at the moment t; it represents the input gate output information at the moment it; Ct−1 represents the information of the memory unit at the moment t−1; and WHI is the weight matrix from the input gate to the forget gate and so on for the rest of the matrices.


### 3.1.2. Detection Algorithm

The basic principle of anomaly detection method can be described as follows. When an anomaly event occurs, the current video frame will be significantly different from the previous video frame. Accordingly, an end-to-end model is trained, which consists of a spatial feature extractor and a temporal encoder-decoder that jointly learn the temporal patterns of the input video sequence. The model is only trained on videos consisting of normal scenes, with the goal of minimizing the reconstruction error between the input and output videos reconstructed by the learned model. After proper training of the model, normal videos are expected to have low reconstruction errors, while videos containing abnormal scenes are expected to have high reconstruction errors. Anomalies are adaptively detected by thresholding the error produced by each test input video.(1) Autoencoder. Autoencoder learns regular patterns in training videos based on the autoencoder framework, which consists of two parts, namely, a spatial autoencoder for learning the spatial structure of each video frame and a temporal encoding for learning the temporal structure. The spatial encoder and decoder consist of two convolutional and deconvolutional layers, respectively, while the temporal encoder-decoder consists of a three-layer ConvLSTM model. It takes a sequence input of length T and outputs the reconstructed input sequence. The rightmost number represents the output size of each layer. The spatial encoder takes one frame as input at a time, and after processing T=4 frames, the encoding features of the T frames are concatenated into temporal encoding for motion encoding. The decoder mirrors the encoder to reconstruct the input video volume. An autoencoder consists of two parts: encoding and decoding. It reduces dimensionality by setting the number of encoder output units smaller than the input and is trained with an unsupervised backpropagation method to minimize the reconstruction error of the decoding results.The main purpose of the convolutions in convolutional networks is to extract features from an input image. The convolutional network will automatically learn the value of the filter during the training process, but parameters such as the number, size, and number of layers of the filter need to be specified before training. As the number of filters increases, more image features can be extracted, and the recognition performance of the network becomes much better. But more filters will increase computation time and run out of memory faster, and applications need reasonable settings to achieve a balance between accuracy and speed.(2) Reconstruction Error Calculation. The trained model can be used to obtain reconstructions of the input video sequence. The reconstruction error is represented by
(6)e=1np∑k=1n∑i=1pθ∧ki−θki2,where θ^ki is the output pixel value; θki is the input pixel value; p is the total number of pixels per frame; and n is the number of frames.A regularity score for the video can be calculated based on the error value. The regularity score normalizes the reconstruction error for each video sequence between 0 and 1. The regularity score of a sequence is given by(7)gx=1−ex−minxexmaxxex,where x is the output reconstruction sequence and ex is the reconstruction error of the sequence. Video sequences containing normal events have high regularity scores because they are similar to the data used to train the model, while sequences containing abnormal events have lower regularity scores. Accordingly, abnormal events found in the video can be detected, and corresponding results can be obtained.


## 3.1.1. Modified LSTM

Recurrent neural network (RNN) works like a feedforward network, except that the value of its input vector is affected not only by the input vector but also by the entire input history. In theory, RNN can use information in arbitrarily long sequences. But in practical implementation, they can only go back a few steps due to vanishing gradients. To overcome this problem, a variant of RNN, the LSTM model, is introduced, as shown in Figure2. Using this new structure, LSTM prevents the error diffusion and explosion of backpropagation so long sequences can be processed. And they can be stacked together to capture higher-order information.Figure 2
Internal structure of LSTM.In the figure,c is the cell state, which acts like an information pipeline and runs through the entire operation cycle of LSTM. The three gate structures of LSTM can delete and add information in cells, so that information can flow selectively. σ is a nonlinear activation function, which maps the output value of the function between 0 and 1, where 0 means no information passes and1 means all information passes.The convolutional LSTM (ConvLSTM) model is an improved form of the LSTM architecture, and the matrix operation is replaced by convolution compared to the general LSTM. By using convolutions for input-to-hidden and hidden-to-hidden connections, ConvLSTM requires fewer weights and produces better spatial feature maps. The formula of the ConvLSTM unit can be summarized as follows:(1)ft=σWXF∗Xt+WHF∗ht−1+WCF∘Ct−1+bF,where ft is the output value of the forget gate, which decides to delete or forget part of the information from the storage unit; ht−1 is the output value at the previous moment; Xt is the current input value; W and b are the matrix of coefficients and the vector of bias, respectively; and σ is the sigmoid activation function.
(2)it=σWXI∗Xt+WHI∗ht−1+WCI∘Ct−1+bI.Equation (2) is used to calculate the value of the input gate it to determine how much information to update, and the output is a value between 0 and 1.
(3)Ct=ft∘Ct−1+it·tanhWXC∗Xt+WHC∗ht−1+bC.Equation (3) is used to update the cell state. The old cell state Ct−1 is multiplied by the output value of the forget gate ft, the output updated postselection value expression is multiplied by the input gate it, and the two are added to obtain the new cell state Ct.
(4)Ot=σWXO∗Xt+WHO∗ht−1+WCO∘Ct+bo.Equation (4) is used to calculate the value of the output gate Ot, which determines how much memory is used for the output.
(5)ht=Ot∘tanhCt.The final step is to use the hyperbolic tangent function to update the valueCt to be between -1 and 1 and multiply the output gate value Ot with it to get the final output value ht at time t.In the above equations,∗ represents the convolution operation; ∘ represents the Hadamard product; Xt represents the input image at the moment t; it represents the input gate output information at the moment it; Ct−1 represents the information of the memory unit at the moment t−1; and WHI is the weight matrix from the input gate to the forget gate and so on for the rest of the matrices.


## 3.1.2. Detection Algorithm

The basic principle of anomaly detection method can be described as follows. When an anomaly event occurs, the current video frame will be significantly different from the previous video frame. Accordingly, an end-to-end model is trained, which consists of a spatial feature extractor and a temporal encoder-decoder that jointly learn the temporal patterns of the input video sequence. The model is only trained on videos consisting of normal scenes, with the goal of minimizing the reconstruction error between the input and output videos reconstructed by the learned model. After proper training of the model, normal videos are expected to have low reconstruction errors, while videos containing abnormal scenes are expected to have high reconstruction errors. Anomalies are adaptively detected by thresholding the error produced by each test input video.(1) Autoencoder. Autoencoder learns regular patterns in training videos based on the autoencoder framework, which consists of two parts, namely, a spatial autoencoder for learning the spatial structure of each video frame and a temporal encoding for learning the temporal structure. The spatial encoder and decoder consist of two convolutional and deconvolutional layers, respectively, while the temporal encoder-decoder consists of a three-layer ConvLSTM model. It takes a sequence input of length T and outputs the reconstructed input sequence. The rightmost number represents the output size of each layer. The spatial encoder takes one frame as input at a time, and after processing T=4 frames, the encoding features of the T frames are concatenated into temporal encoding for motion encoding. The decoder mirrors the encoder to reconstruct the input video volume. An autoencoder consists of two parts: encoding and decoding. It reduces dimensionality by setting the number of encoder output units smaller than the input and is trained with an unsupervised backpropagation method to minimize the reconstruction error of the decoding results.The main purpose of the convolutions in convolutional networks is to extract features from an input image. The convolutional network will automatically learn the value of the filter during the training process, but parameters such as the number, size, and number of layers of the filter need to be specified before training. As the number of filters increases, more image features can be extracted, and the recognition performance of the network becomes much better. But more filters will increase computation time and run out of memory faster, and applications need reasonable settings to achieve a balance between accuracy and speed.(2) Reconstruction Error Calculation. The trained model can be used to obtain reconstructions of the input video sequence. The reconstruction error is represented by
(6)e=1np∑k=1n∑i=1pθ∧ki−θki2,where θ^ki is the output pixel value; θki is the input pixel value; p is the total number of pixels per frame; and n is the number of frames.A regularity score for the video can be calculated based on the error value. The regularity score normalizes the reconstruction error for each video sequence between 0 and 1. The regularity score of a sequence is given by(7)gx=1−ex−minxexmaxxex,where x is the output reconstruction sequence and ex is the reconstruction error of the sequence. Video sequences containing normal events have high regularity scores because they are similar to the data used to train the model, while sequences containing abnormal events have lower regularity scores. Accordingly, abnormal events found in the video can be detected, and corresponding results can be obtained.


## 3.2. Biomedical Signal Abnormal Judgment Method

On the basis of anomaly detection in surveillance video, the system will automatically determine the identity of the perpetrator and then associate the person’s health database. Through the health detection sensors carried by the elderly, biomedical signal characteristics such as heart rate, blood pressure, and pulse of abnormal behavior can be obtained. According to its health database, it can be determined whether the current heart rate, blood pressure, pulse, and other parameters are within its allowable range, and the physical state of the abnormal behavior person can be determined accordingly. Taking blood pressure parameters as an example, the normal range is [P1, P2] based on the analysis of the abnormal behavior health database. When the blood pressure value returned at this time exceeds the above range, it is considered that there is a risk in the physical state of the person and measures need to be taken in time. At the same time, different levels of risk, such as low risk, medium risk, and high risk, can be set according to the size of the out of range, and different solutions can be adopted accordingly. For other biomedical signal parameters, corresponding methods can be used to judge, and the final fusion decision is the corresponding risk level.


## 4. Conclusion

In view of the current hot issues of old age care, combined with the real background of elderly care issues, this paper applies AI technology to elderly care and designs an intelligent nursing system for the elderly. The system is based on the intelligent anomaly detection algorithm and uses the video anomaly detection algorithm to find the abnormal behavior of the elderly under video surveillance. Further, the system uses the biological signal anomaly detection method to determine the physical state of the elderly associated with the abnormal event, to determine possible unexpected situations, and to take corresponding countermeasures. With the assistance of the smart nursing system, it can greatly provide the pertinence and overall efficiency of nursing work for the elderly and effectively reduce the burden on medical staff.



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*Source: 7427968-2022-08-21.xml*