Design of community intelligent remote health monitoring system

1 Introduction

人口老龄化已成为当今世界的一个突出的社会问题。据联合国统计，2002年，全世界的老龄人口（60岁及60岁以上）总数已达6.29亿，占世界人口总数的10％，到2050年，将占总人口数的21％。中国是世界上老年人口最多的国家，2004年底，中国60岁及以上老年人口已达1.43亿，占总人口的10.97%。到2050年，老龄化水平将推进到30%以上，其中，80岁及以上老年人口将占老年人口的21.78%[1]。随着老年期的延长，因疾病、伤残、衰老而失去生活能力的老年人显著增加，医疗保障面临严峻挑战[2]。

In order to alleviate the medical pressure brought by the aging population, enable the elderly and disabled who lack self-care ability to better integrate into family life, and reduce the care burden of medical staff and their families, we proposed a video-based community intelligent remote health monitoring system that integrates nursing, remote health monitoring and remote diagnosis, and studied the key technologies of video remote health monitoring and remote diagnosis in the process of video remote health monitoring and remote diagnosis, such as video coding and data network transmission, so as to promote the research progress and popularization of community remote health monitoring systems and alleviate the increasingly serious problem of population aging in my country.

2 Video-based community intelligent remote health monitoring system

The video-based community intelligent remote health monitoring system introduced in this paper uses intelligent nursing beds and supporting physiological parameter monitors as home clients, real-time detection of human physiological parameters, video monitoring of the physical condition of the monitored object, automatic data collection, real-time analysis of the health status of the monitored object, and alarm to the medical center in case of abnormal conditions to obtain timely assistance. The system can realize information interaction and sharing with the medical information system of existing hospitals to form a medical, nursing and disease prevention network covering urban areas. In addition, the system can realize remote video conversations between doctors and monitored objects, allowing patients to be diagnosed and treated in their familiar home environment, which not only reduces the psychological pressure of patients, improves the accuracy of diagnosis, but also facilitates the recovery of the disease. At the same time, it reduces the economic burden of patients due to long-term hospitalization. In addition to the elderly, the main monitoring objects of the system can also be disabled people, postoperative recovery patients, and high-incidence populations.

2.1 System Hardware Composition

As shown in Figure 1, the community intelligent remote health monitoring system is mainly composed of a home client and a community remote monitoring center. The home client and the community remote monitoring center are connected through a network.

Figure 1 Community intelligent remote health monitoring system based on video

The hardware equipment of the home client consists of an intelligent nursing bed, a physiological parameter monitor, a personal computer, a zoom camera, a controllable universal pan/tilt, a video capture card and a network interface device. The hardware equipment of the community remote monitoring center mainly consists of a monitoring server and a network interface device.

2.2 System Functions

The remote intelligent health monitoring system mainly includes three functions: monitoring, nursing and remote diagnosis of the monitored objects.

(1) Monitoring function: The physiological parameter monitor continuously detects the physiological parameters of the monitored object for a long time and transmits the data to the remote monitoring center server through the network. The remote center server processes and analyzes the data in real time to identify early signs of disease, abnormal conditions and changes in the health status of the monitored object. This system can monitor six physiological parameters: electrocardiogram, blood pressure, blood oxygen, pulse, respiration and body temperature. Through video, the community remote monitoring center can observe the physical and mental state of the monitored object in real time.

(2) Nursing function: The smart nursing bed used by the home client is a smart nursing bed developed by the Robotics Research Laboratory of South China University of Technology that integrates monitoring and nursing. It can help the monitored person complete different body position changes such as lying flat, raising the back, raising and lowering the legs, sitting, turning over, and defecating according to key or voice control commands, and assist in completing some of the nursing work for the monitored person.

(3) Remote diagnosis: The system uses video monitoring technology. With the permission of the monitored person, the system can remotely observe the physical condition of the monitored person, understand the physical condition of the monitored person in real time, and make a remote diagnosis in combination with other physiological parameter data. During the diagnosis process, doctors and patients can have remote interactive dialogues, which greatly improves the efficiency and accuracy of diagnosis. In order to protect personal privacy, the home client can control the switch of the video monitoring system.

3 Key technologies of remote video monitoring

The workflow of remote video monitoring is shown in Figure 2. First, the client camera acquires the video signal, and then performs effective video encoding on the acquired video signal. The encoded video is transmitted to the community remote monitoring center server through the network. After receiving the video data, the central server decodes it and then displays the video. The key technologies in this process mainly include video encoding and video network transmission. Video technology is applied to community intelligent remote health monitoring, which can realize remote interactive diagnosis between doctors and patients. Through the transmission of video images, doctors can understand the patient's condition and mental state in real time. However, video image information also brings the problem of large data transmission to the network. In order to ensure the real-time monitoring of the system and reduce the burden of network transmission, this paper adopts the latest digital video coding international standard H.264/AVC to encode the video image, and then realizes the transmission of video data between the remote monitoring server and the home client through VC++ programming. The receiver decodes the compressed video image and then reproduces the video image on a given window, thereby realizing the real-time transmission of video information in the community intelligent remote health monitoring system.

Figure 2 Remote video monitoring workflow

3.1 Video Coding Based on H.264/AVC

As a new generation of international video coding standard. H.264/AVC has the characteristics of high coding efficiency and strong network adaptability. It not only saves 50% of the bit rate compared with H.263 and MPEG-4, but also has better support for network transmission [3][4]. Therefore, this paper adopts H.264/AVC to encode the video data generated in community intelligent remote health monitoring. H.264/AVC was first applied in the field of real-time video communication, such as conference television and videophone. It introduces an IP packet-oriented coding mechanism, which is conducive to packet transmission in the network and supports streaming media transmission of video in the network. H.264/AVC has strong error-resistant characteristics and can adapt to video transmission in wireless channels with high packet loss rate and severe interference, thereby obtaining smooth image quality. Moreover, the basic system of H.264/AVC does not require the use of copyright and is open in nature.

Figure 3 H.264/AVC encoding framework

The coding framework of H.264/AVC is shown in Figure 3. Like previous standards, H.264/AVC still follows the block-based hybrid coding framework. First, the input image is divided into macroblocks, and redundant information is removed by spatial or temporal prediction. Then, the prediction residual is orthogonally transformed to obtain a series of transform coefficients. The transformation makes the residual coefficient energy relatively concentrated, and then quantization is performed, and different data amounts are allocated to describe it according to the energy contained. Finally, entropy coding is used for binary encoding to minimize the symbol redundancy of the quantized data. The decoding process is the inverse process of encoding. For details of H.264/AVC, please refer to reference [5].

3.2 Implementation of data network transmission

The data transmission between the home client and the community remote monitoring center is implemented based on Windows Socket. Windows Socket supports two types of sockets, stream sockets and data sockets. Stream sockets are widely used due to their high reliability. This system uses stream sockets. The central server establishes a listening socket and a connection socket queue, and the home client establishes a communication socket. The central server first enters the listening state. When receiving a connection request from the home client, it establishes a connection socket for communication. If other clients send connection requests, another connection socket is established and added to the connection socket queue, thereby realizing data transmission between the central server and multiple clients. The process of network transmission of monitoring data is shown in Figure 4.

Figure 4 Monitoring data network transmission flow chart

Figure 5: Software operation interface of the community intelligent remote health monitoring system

The software operation interface of the video-based community intelligent remote health monitoring system is shown in Figure 5. The software is implemented by Visual C++ programming. The interface can display the dynamic curves of the user's physiological parameters and the video information sent by the client in real time. The video window can be enlarged, reduced, and closed as needed.

4 Conclusion

The video-based community intelligent remote health monitoring system constructed in this paper can greatly alleviate the shortage of medical resources caused by the aging population. By real-time detection of health-related physiological parameters of the elderly and disabled people in nursing beds, the community monitoring center can timely grasp the sudden changes in the physical condition of the monitored objects and further understand the details of the monitored objects through video interaction, and put forward corresponding medical suggestions. At the same time, a physical status database of the medical objects is established for reference during medical diagnosis. Remote monitoring of important physiological parameters of patients can not only assist medical treatment, but also alarm when the condition suddenly deteriorates. With the advancement of science and technology, the aggravation of the aging population problem and the continuous improvement of people's requirements for medical care, the community intelligent remote health monitoring system will surely be widely used.