Application of Multi-sensor Network and Arti�cial Intelligence in Health Monitoring of Medical Geriatric Care

The comprehensive use of data from multiple sensors for moving target tracking is a long-term problem in multi-sensor data fusion. In wireless sensor networks, state estimation and fusion are the main research issues in target tracking. Distributed is the essential feature of wireless sensor networks. Therefore, with the development of wireless sensor networks, the problem of distributed state estimation has also attracted the attention of scholars. The intelligent medical diagnosis system is composed of a traditional Chinese medicine diagnosis system, a western medicine diagnosis system, and a medical record database. The TCM diagnostic system and the Western medicine diagnostic system are the main body of the system. The TCM diagnostic system is based on a case-based reasoning model, using human body information collection equipment to simulate the process of TCM diagnostics and realize TCM diagnostic engineering, while the Western medicine diagnostic system is a neural network optimized based on genetic algorithms. Model, obtain the diagnosis and treatment method of the disease from the medical record database of the hospital information system. The basic standards for nursing homes issued by the Ministry of Health have promoted the standardized development of nursing homes. However, since the development of China's elderly care homes is still in its infancy, it makes the service content and functional positioning of elderly care homes, and orderly connection with relevant medical institutions and elderly care institutions, The main body of institutional management and the formulation of supporting policies are still in the exploratory stage. Among the chronically ill population, the proportion of elderly people ranks �rst. At the same time, due to the particularity of the elderly population, the recovery time is lengthened. Due to various reasons, they cannot get adequate medical services in the hospital. Due to the impact of diseases, the health of the elderly is not optimistic, and even loses their lives. Therefore, the daily immediate monitoring of the elderly is very necessary.


Introduction
The Kalman lter algorithm is a classic method of state estimation in linear systems. The average consistency strategy is an effective method for network-wide distributed computing tasks. In recent years, scholars have combined the Kalman lter and the consensus protocol to propose the Kalman consensus lter to improve the estimation performance by improving the consistency of the estimation of different nodes, and due to the simple distributed structure, strong scalability and robustness of the algorithm So it has attracted widespread attention [1]. According to the characteristics of Chinese medicine lacking machine inspection methods, the article adopts the method of human body information collection to simulate the operation of Chinese medicine pulse number, and realizes the inspection in the way of Chinese medicine, and the inspection results are quantitatively saved in the Chinese medicine case database [2]. At the same time, based on the characteristics of Chinese medicine, a case-based reasoning model is used to establish an expert system for Chinese medicine diagnosis. Use the growing case base as the knowledge base for reasoning and realize the diagnosis function of Chinese medicine [3]. Therefore, this paper designs a diagnosis model based on neural network, makes full use of the existing hospital diagnosis system, and uses the hospital's existing case database as network training samples to realize the diagnosis function of western medicine [4]. It has become an important issue that needs to be solved urgently, and it has also been a hotspot and focus of research in various circles in recent years [5]. This article is oriented to the health monitoring of the elderly, and has developed an intelligent monitoring terminal with no sense of violation, including a wearable vital sign monitoring terminal and a distributed plantar pressure collection terminal, which realizes the vital sign data of the elderly in free activities [6]. And gait information collection and analysis. The vital signs monitoring terminal adopts STM32 singlechip microcomputer as the microcontroller, and the ECG acquisition circuit, respiratory wave acquisition circuit, photoelectric sensor temperature acquisition circuit and wireless communication circuit, etc., construct a portable and easy-to-operate monitoring terminal [7]. The distributed plantar pressure acquisition terminal adopts a cross-switching method. Through the cooperation of software and hardware, the distributed plantar pressure data is transmitted to the PC via the wireless network for display and analysis [8].

Related Work
The use of data from multiple sensors for moving target tracking is a long-term problem in multi-sensor data fusion. In military applications such as target tracking or tactical intelligence, it is mainly to estimate and predict the state of speci c types of entities in the external environment [9]. The literature says that the traditional method is to gather the observation data or local estimation information of each sensor node to the fusion center, and the fusion center will perform the data fusion processing to obtain the global state estimation [10]. At the same time, remote hospitals are still short of professional medical staff [11]. This also leads to a high rate of misdiagnosis in these hospitals, especially for some intractable diseases. In addition, in some small hospitals with low medical level and lack of conditions, misdiagnosis and delay of serious diseases often occur, which may endanger the lives of patients. Therefore, the intelligent diagnosis system can avoid this situation to a certain extent [12]. At present, with the continuous development and progress of computer technology, intelligent medical diagnosis system can be applied in the application of hospital system.The literature is limited by the decline in the physical tness of the elderly, more likely to get sick, and the rehabilitation process is lengthened, the society's attention to elderly health monitoring has increased signi cantly. The number of elderly patients with chronic diseases shows an increasing trend every year. Various reasons lead to the inability to receive adequate medical services in the hospital. Affected by the disease, the health of the population is not optimistic and even loses their lives. Therefore, it is particularly important to carry out immediate monitoring around the clock [13]. The literature points out that the health of the elderly embodies the characteristics of "high incidence rate, diverse disease types, long course of disease, high complications, and di culty in treatment", which makes the elderly whose body functions degenerate have low self-care ability and high dependence [14]. There is a strong demand for nursing services; but families and elderly care institutions cannot bear the burden of elderly medical care. The literature mentions that China has unique characteristics of aging, such as getting older before getting rich, larger scale, faster speed, and heavier support burden. In addition, China's old-age security system needs to be further improved [15].
Therefore, under the severe challenge of population aging, Chinese society is facing tremendous pressure on the elderly.

Multi-sensor network algorithm
Using multi-sensor data to estimate the state of a target is one of the main tasks of data fusion. Kalman lter method is a classic method in state estimation. Standard Kalman lter and information Kalman lter are two forms of Kalman lter. This chapter will give two speci c derivation and proof procedures. In addition, in multi-sensor estimation and target tracking, a central fusion processing structure is often used. A more natural approach is to extend the single-sensor KF method to multi-sensor problems. This chapter will give examples of group sensor methods and inverse covariance .
For dynamic state estimation, discrete time methods are widely used. In order to analyze and infer dynamic systems, at least Description of the initial state: Assume that the initial state X(0) is Gaussian, with mean X(0|0) and covariance P(0|0) and 1 Where 2 The physical process of a moving object can be divided into two parts: 1) the part that can be accurately predicted according to the equation of motion; 2) a random process with a mean value of zero and a known distribution. The Kalman lter is based on this theory to establish a motion model. According to this mathematical description, the position of a moving object at any time can be estimated.
Kalman ltering includes a prediction phase and an update phase. In the prediction phase, the optimal state estimate at the previous moment is used to predict the current state value according to the system state model, and the observation value at the current moment is predicted according to the observation model; in the update phase, Use the state value measured at the current moment to optimize the state prediction value obtained in the prediction stage, so as to obtain the nal estimated value at the current moment.
In multi-sensor integrated tracking, the inverse covariance is often used to express the state estimation covariance update equation, which has a recursive formula: 3 The lter gain formula has another form of expression: 4 The Kalman lter of a discrete system is a linear unbiased recursive lter. Linear means that the output of the lter is a linear function of the observed value. Unbiased means that the state estimate is equal to the mean value of the actual state. Recursive means this estimate The value can be obtained by revising the previous estimated value by using the new observation value. Therefore, the Kalman lter has properties such as linearity, unbiasedness and recurrence.

Application of arti cial intelligence technology in medical systems
Modern medicine is divided into categories, resulting in extremely complicated procedures for medical treatment in hospitals. The diagnosis and treatment process of a hospital is shown in Fig. 1.
The diagnosis and treatment process of Chinese medicine is relatively simple. The top three hospitals have TCM departments, but the TCM departments are smaller in scale, and TCM uses fewer diagnosis and treatment methods. It can be said that Chinese medicine is completely different from Western medicine in terms of diagnosis and treatment, to diagnosis and treatment methods, and then to treatment process. Excellent TCM doctors can prescribe the right medicine to cure the disease by just a few simple diagnostic methods such as inquiries, tongue coating, and pulse identi cation. The design of a medical diagnosis system must fully consider the difference between Chinese and Western medicine, otherwise it will result in a four-different system of "painting a tiger is not an anti-dog".
The work ow of the medical diagnosis system is shown in Fig. 2.
If Western medicine is selected for diagnosis and treatment, the system will list a checklist based on the physical information selected by the patient, and the patient's check result will be transmitted to the diagnosis system through the hospital's existing information management system. According to the checklist, the diagnostic system calls the western medicine diagnostic subsystem based on neural networks to give a treatment plan. The treatment plan calls the western medicine medication system to synthesize the current development of western medicine and the clinical manifestations and curative effects of each medicine, and give medication recommendations, which are the same as the diagnosis of traditional Chinese medicine. The system is similar. The western medicine diagnostic subsystem also enters a cycle of inspection and treatment until the patient is discharged from the hospital and the medical record is printed. Nursing work has the characteristics of high pressure, high labor intensity, and high risk responsibility due to its own particularities. 49.1% of the interviewees expressed a lot of mental and work pressure during work. 24.6% of the interviewees said that they are under a lot of pressure from public opinion at work, and they are unwilling to mention their work to others. In the interviews, some medical staff even said that their current work is rejected and incomprehensible by their family members. Despite working under such a heavy load, medical staff in elderly nursing homes still have problems such as unequal pay and income, limited development space, and lack of professional skills training, as shown in Table 2. Government departments repeat assessments and waste management resources: The health bureau, civil affairs bureau, social security bureau, and price bureau still have overlapping functions and overlapping functions in actual operation, which leads to the problem of overlapping inspection content, resulting in the human, nancial and material resources of various management departments And other waste.

Medical Elderly
Relevant rules and regulations are not sound, and institutional operations are not standardized: the lack of admission standards; the current relevant government departments have not promulgated uniform discharge standards; the lack of transfer standards at this stage.
Insu cient economic compensation and slow institutional development: due to policy regulations that are too broad and general, and the lack of coordination among various departments, lack of a uni ed management department responsible for policy implementation and supervision and management; some elderly care home services are not included in the scope of medical insurance reimbursement; elderly groups The huge demand for medical care services has prompted social capital to actively invest in new aged care homes. However, due to the nature of public products in the medical care services provided by aged care homes, the investment in aged care homes has a high investment amount, a long payback period, and bene ts. Slow characteristics.The lack of a nursing insurance system makes it di cult to protect the health rights and interests of the elderly: the differences in the economic abilities of individuals and families have caused gaps in the ability of the elderly to enjoy medical care.
Strengthen the reserve and cultivation of nursing talents, and improve professional quality: The government, as a provider and guarantor of public products and services, should continuously increase the investment and training of nursing talents, and strictly implement the "certi cated work" system for elderly nursing homes. Colleges and universities should adjust, enrich and optimize the teaching curriculum according to the training level of nursing talents. Elderly nursing homes should also regularly carry out training and continuing education based on the problems exposed in their own operations and the work needs of medical staff. At the same time, in order to ensure the orderly provision of elderly care services, elderly nursing homes should strictly hold a certi cated employment system. Control the quality of care from the source.
First, a nursing insurance system with mutual aid and adjustment functions. At the same time, the government also encourages the development of commercial nursing insurance through economic leverage and supporting policies; second, the source of insurance funds should be implemented by the government, society, and individuals. The principle of simultaneous payment, and can be managed by combining old-age care insurance, endowment insurance, and medical insurance. At the same time, the government grants nancial subsidies to the poor and exempts them from paying insurance premiums; thirdly, the payment model is reasonably determined through the classi cation and grading of nursing care. Through the establishment of a scienti c and standardized evaluation system, the nursing services provided are divided, and the corresponding charging system is established, so as to improve the quality of nursing services and the e ciency of supply.

Systematic design of elderly health monitoring
Power frequency interference-the space electromagnetic interference generated by the power supply equipment in the environment, the frequency is 50 Hz, the components are sinusoidal signals and harmonics, and the maximum amplitude is close to half of the ECG peak value. As shown in Fig. 3, the superposition of sinusoidal signals on the ECG graph is power frequency interference.
As shown in Fig. 4, baseline drift is usually caused by low-frequency interference, such as human respiration and electrode movement. The baseline variation amplitude at low frequencies is 15% of the ECG peak value.
As shown in Fig. 5, the EMG baseline is relatively obscure, and the voltage uctuation is small. Features: The instantaneous energy is concentrated at 30-300Hz.
X-ray diffraction (XRD) measurement was performed on polyester fabric and graphene textile samples with an X-ray diffractometer, using CuKa radiation (λ = 0.15418nm), V = 30kV, I = 30mA. In the range of 5-60º, the scan rate is maintained at 0.1s-1/2θ, and the result is shown in Fig. 6. The diffraction curve of polyester textiles and graphene textiles are typical patterns of polyester fabrics. Due to the low concentration of graphene, it is di cult to nd characteristic peaks of graphene in the patterns of modi ed polyester fabrics.
The process of thin-lm pressure sensor: advanced screen printing. Production process: rst attach conductive silver paste, nano-force sensitive semiconductor ink, etc. on the lm substrate, and then make it through a special process. Working principle: As the external pressure increases, the resistance value of the corresponding pressure coating becomes smaller and smaller. Through software control, the pressure is collected and processed, and the pressure size and dynamic distribution can be obtained. The technical parameters of the membrane pressure sensor are shown in Table 3. The determined coordinates of all points are shown in Table 4. For example, the actual coordinates of point 1 are (11,32), and the strobe coordinates are (IO_B, OUT_1). When the point "1" is collected, IO_B is strobed. At this time, the output of OUT_1 The value is the voltage value at that point. Hospital diagnosis and treatment process Page 14/16 The work ow of the medical diagnosis system Page 15/16

Figure 3
Power frequency interference graph Raman spectrum and X-ray diffraction spectrum of graphene fabric. (a) Raman spectrum and reduction of graphene textiles, (b) X-ray diffraction (XRD) curves of polyester textiles and graphene textiles.