Health monitoring, early warning and alarming system for home-based care for aged

Abstract

The invention is suitable for the technical field of home-based care health monitoring, and provides a home-based care health monitoring, early warning and alarming system, which comprises a multi-component data acquisition module, a health database storage module, a grading early warning module and a service response execution module, the multi-component data acquisition module performs data transmission with the health database storage module through wireless signals, and the health database storage module performs information interaction with the grading early warning module through wireless signals. According to the health monitoring, early warning and alarming system for home-based care for the aged, the health monitoring, early warning and alarming system for home-based care for the aged is composed of the multi-component data acquisition module, the health database storage module, the grading early warning module and the service response execution module, so that health monitoring can be carried out on the aged for home-based care; integrated monitoring is carried out on old people health management and health monitoring big data collection, storage, analysis and research and judgment, a basis is provided for old people health risk prevention, and an old people health risk classification and grading service response system is constructed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of home health monitoring for the elderly, and particularly relates to a home health monitoring and early warning alarm system for the elderly. BACKGROUND

[0002] With the increase in the number of elderly people, nursing resources are increasingly insufficient. When a solitary old person is ill, he or she may not be able to receive timely care. A solitary old person may also face safety risks, such as no one knowing about a fall or an emergency situation such as a gas leak being unable to be handled in time. The physical functions of elderly people deteriorate seriously, and they need more life care. Children cannot always be by their side, which can easily lead to inadequate care.

[0003] The further deepening of the aging degree brings a series of social problems, among which the problems of solitary, elderly, and sick old people in life care, health care, and spiritual comfort are particularly prominent. The solution to such problems requires further clarification of the whole-process health management responsibility boundary, the motivation of the participation of multiple subjects such as the government, enterprises, society, and individuals, the full integration of communities and medical, nursing, and care resources, the change of the separation status of old-age resources and the needs of the elderly, the development of artificial intelligence technology, especially the application of intelligent monitoring, health management, and daily life assistance, and the difficulty in integrating monitoring and early warning and alarm in the collection, storage, analysis, and research and judgment of old-age health management and health monitoring big data. SUMMARY

[0004] The application provides a home health monitoring and early warning alarm system for the elderly, which aims to solve the problem that in the prior art, old-age health management and health monitoring big data collection, storage, analysis, and research and judgment are difficult to integrate monitoring and early warning and alarm.

[0005] The application is implemented in the following manner. A home health monitoring and early warning alarm system for the elderly includes a multi-component data acquisition module, a health database storage module, a hierarchical early warning module, and a service response execution module. The multi-component data acquisition module transmits data to the health database storage module through wireless signals. The health database storage module and the hierarchical early warning module exchange information through wireless signals. The hierarchical early warning module exchanges information with the service response execution module through wireless signals.

[0006] The multi-component data acquisition module is used to continuously monitor electrocardiogram and blood oxygen vital signs, identify fall and long sitting abnormal movements, and monitor real-time living environment indicators such as temperature and humidity and gas concentration.

[0007] The health database storage module is used to establish a dynamically updated personal health digital twin, integrate electronic medical records, real-time monitoring stream data, and voice and video unstructured data, and store individualized health models.

[0008] The hierarchical early warning module is used for predicting future physiological risks based on an LSTM neural network.

[0009] The service response execution module is used for constructing an intelligent dispatching hub of the pension service circle and performing emergency services according to the early warning information of the hierarchical early warning module.

[0010] Preferably, the multi-component data acquisition module is composed of a physiological parameter monitoring component, a behavior monitoring component and an environment perception component, the physiological parameter monitoring component includes intelligent wearable devices, mattress type vital sign monitoring pads, portable blood glucose and uric acid detectors, the behavior monitoring component includes millimeter wave radars, intelligent mattresses and voice emotion recognition devices, and the environment perception component includes temperature and humidity sensors, gas smoke alarms and infrared human body movement sensors.

[0011] Preferably, the health database storage module is composed of a structured data layer, an unstructured data layer and a knowledge graph layer, the structured data layer is used for integrating historical physical examination, medication and operation records and recording physiological parameters, behavior characteristics and environmental data, the unstructured data layer is used for recording voice emotion logs and video behavior sample libraries, and the knowledge graph layer is used for constructing a disease risk prediction model and a linkage relationship graph of families, communities and medical institutions.

[0012] The hierarchical early warning module is composed of a risk assessment module and a hierarchical trigger module, the risk assessment module is used for dynamic early warning based on an LSTM neural network and static assessment based on an AHRQ fall risk assessment scale, and the hierarchical trigger module is respectively used for physiological parameter abnormality alarm for first-level orange early warning, behavior abnormality alarm for second-level red early warning and environmental crisis alarm for third-level black early warning.

[0013] Preferably, the service response execution module is composed of a first-level response unit and a second-level response unit.

[0014] Preferably, the first-level response unit is a home-based pension service center.

[0015] Preferably, the second-level response unit includes hospitals, social organizations, governments, communities and families.

[0016] Preferably, the wireless signal is transmitted by an encrypted wireless base station.

[0017] Beneficial effects

[0018] Compared with the prior art, the beneficial effects of the present application are: the home-based elderly health monitoring and early warning alarm system of the present application can monitor the health of the elderly at home, integrate the collection, storage, analysis and judgment of the health management and health monitoring big data of the elderly, and early warning alarm, build an early warning and service response mechanism for the health risks of the elderly in the community home-based care, obtain the health monitoring data of the elderly through the intelligent health monitoring equipment, realize the active and passive monitoring of the health of the elderly on this basis, realize the active and sudden event early warning of the health risks of the elderly, and build an elderly health risk prevention service support system based on health early warning and home-based care community service center and institutional response, build a customized monitoring scheme for the health status of the elderly, make it possible to realize low-cost and high-efficiency early warning of the health status of the elderly, secondly, build a "one file" elderly health dynamic monitoring and early warning mechanism to provide a basis for the prevention of health risks of the elderly, and then build a health risk classification and grading service response system for the elderly, which breaks the path between the elderly, home-based care service center, and health service institutions for the elderly, realizes the normalization, intelligence, accuracy and effectiveness of the health early warning of the elderly. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The structure diagram of the home-based elderly health monitoring and early warning alarm system of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0021] Please refer to Figure 1 The present application provides a technical scheme: a home-based elderly health monitoring and early warning alarm system, comprising a multi-component data acquisition module, a health database storage module, a hierarchical early warning module and a service response execution module, the multi-component data acquisition module transmits data with the health database storage module through wireless signals, the health database storage module and the hierarchical early warning module exchange information through wireless signals, and the hierarchical early warning module exchanges information with the service response execution module through wireless signals.

[0022] The multi-component data acquisition module is used to continuously monitor the electrocardiogram and blood oxygen vital signs, identify the abnormal actions of falling and long sitting, and monitor the living environment indicators of temperature, humidity and gas concentration in real time.

[0023] The health database storage module is used to establish a dynamically updated personal health digital twin, which fuses electronic medical records, real-time monitoring stream data and voice and video unstructured data, and stores individualized health models.

[0024] The hierarchical early warning module is used to predict future physiological risks based on an LSTM neural network.

[0025] The service response execution module is used to build an intelligent dispatch hub of the pension service circle, and perform emergency services according to the early warning information of the hierarchical early warning module.

[0026] In the embodiment, the home-based pension health monitoring and early warning alarm system composed of the multi-component data acquisition module, the health database storage module, the hierarchical early warning module and the service response execution module can monitor the health of the elderly in home-based pension, and integrate monitoring and early warning for health management and health monitoring big data collection, storage, analysis and research of the elderly.

[0027] The intelligent technology and digital technology play a monitoring and early warning role in the health risk prevention of the elderly, and on this basis, how to realize efficient, fast, low-cost and accurate service response to help the well-being of the elderly.

[0028] Based on the multi-modal data collection, sorting and analysis technology, a customized health monitoring scheme for the elderly is realized, and based on the linkage mechanism of the old-age service center as the link, the old-age health service mechanism is linked, which is a breakthrough to the existing old-age individual health risk that cannot realize normal early warning and the point-to-point connection of the old-age health service point, and has certain innovation, applicability and popularization.

[0029] Further, the multi-component data acquisition module is composed of a physiological parameter monitoring component, a behavior monitoring component and an environment perception component, the physiological parameter monitoring component includes intelligent wearable devices, mattress type vital sign monitoring pads, portable blood glucose and uric acid detectors, the behavior monitoring component includes millimeter wave radars, intelligent mattresses and voice emotion recognition devices, and the environment perception component includes temperature and humidity sensors, gas and smoke alarms and infrared human body movement sensors.

[0030] In the embodiment, the intelligent wearable device can be a heart rate, blood oxygen and blood pressure bracelet, and the mattress type vital sign monitoring pad monitors the respiration, heart rate and body movement of the person.

[0031] The millimeter wave radar is used for fall detection and activity trajectory analysis, the intelligent mattress is used for out-of-bed and long-sitting early warning, the voice emotion recognition device is used for detecting abnormal voiceprints, the temperature and humidity sensor is used for preventing heatstroke and low temperature early warning, the gas and smoke alarm is used for fire and gas leakage monitoring, and the infrared human body movement sensor is used for long-term inactivity detection.

[0032] Further, the health database storage module is composed of a structured data layer, an unstructured data layer and a knowledge graph layer. The structured data layer is used to integrate historical physical examination, medication and surgery records, and record physiological parameters, behavioral characteristics and environmental data. The unstructured data layer is used to record voice emotion logs and video behavior sample library. The knowledge graph layer is used to build a disease risk prediction model and a linkage graph of families, communities and medical institutions.

[0033] In the embodiment, the voice emotion log is analyzed through call recording, and the video behavior sample library records fall and abnormal action video clips.

[0034] The hierarchical early warning module is composed of a risk assessment module and a hierarchical triggering module. The risk assessment module is used for dynamic early warning based on an LSTM neural network and static assessment based on an AHRQ fall risk assessment scale. The hierarchical triggering module is respectively used for physiological parameter abnormality alarm for first-level orange early warning, behavior abnormality alarm for second-level red early warning and environmental crisis alarm for third-level black early warning.

[0035] In the embodiment, the physiological parameter abnormality in the first-level early warning includes heart rate > 120 bpm for 10 minutes, the behavior abnormality in the second-level early warning includes fall detection and 30 minutes of no movement, and the environmental crisis in the third-level early warning includes gas leakage and human movement detection.

[0036] Further, the service response execution module is composed of a first-level response unit and a second-level response unit.

[0037] Further, the first-level response unit is a home-based care service center.

[0038] In the embodiment, the home-based care service center terminal video is connected, and the service personnel of the home-based care service center is notified to quickly go to the door.

[0039] Further, the second-level response unit includes a hospital, a social organization, a government, a community and a family.

[0040] Further, the wireless signal is transmitted by an encrypted wireless base station.

[0041] In the embodiment, the encrypted wireless base station performs privacy protection on user information, and records the whole response process to a blockchain storage platform.

[0042] The early warning and service response mechanism of the health risk of community home-based care elderly people is constructed. Through the elderly health intelligent monitoring equipment, the elderly health monitoring data is obtained, the active and passive monitoring of the elderly health is realized, and the active and sudden event early warning of the elderly health risk is realized.

[0043] The old-age health risk prevention service support system based on health early warning and home-based care community service center and institutional response is constructed, and the old-age health condition classification and customization monitoring scheme is constructed, so that the old-age health condition realizes the low-cost and high-efficiency early warning.

[0044] The old-age health risk classification and grading service response system is constructed, the path among the old people, the home-based care service center, the old-age health service institution and the like is broken through, the normalization, the intelligence, the accuracy and the effectiveness of the old-age health early warning are realized.

[0045] The working principle and use flow of the present application: after the present application is installed, the home-based care health monitoring early warning alarm system composed of the multi-component data acquisition module, the health database storage module, the grading early warning module and the service response execution module can perform health monitoring on the old people in home-based care, integrates the old-age health management and health monitoring big data collection, storage, analysis and research and judgment and performs early warning and alarm, constructs the early warning and service response mechanism of the old-age health risk of community home-based care, obtains the old-age health monitoring data through the old-age health intelligent monitoring equipment, realizes the active and passive monitoring of the old-age health on the basis, realizes the active normalization early warning and sudden event early warning of the old-age health risk, and constructs the old-age health risk prevention service support system based on health early warning and home-based care community service center and institutional response, constructs the old-age health condition classification and customization monitoring scheme, so that the old-age health condition realizes the low-cost and high-efficiency early warning, then constructs the old-age health risk classification and grading service response system, breaks through the path among the old people, the home-based care service center, the old-age health service institution and the like, realizes the normalization, the intelligence, the accuracy and the effectiveness of the old-age health early warning.

[0046] The above only describes the preferred embodiment of the present application, and does not limit the present application, any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A home-based elderly care health monitoring and early warning alarm system, comprising a multi-component data acquisition module, a health database storage module, a hierarchical early warning module, and a service response execution module, characterized in that: The multi-component data acquisition module transmits data to the health database storage module via wireless signals. The health database storage module interacts with the hierarchical early warning module via wireless signals. The hierarchical early warning module interacts with the service response execution module via wireless signals. The multi-component data acquisition module is used to continuously monitor vital signs such as electrocardiogram and blood oxygen, identify abnormal movements such as falls and prolonged lying down, and monitor living environment indicators such as temperature, humidity and gas concentration in real time. The health database storage module is used to establish a dynamically updated personal health digital twin, integrating electronic medical records, real-time monitoring streaming data, and unstructured voice and video data to store individualized health models; The graded early warning module is used to predict future physiological risks based on an LSTM neural network; The service response execution module is used to build an intelligent dispatch center for the elderly care service circle and to execute emergency services based on the warning information from the hierarchical warning module.

2. The home-based elderly care health monitoring and early warning alarm system as described in claim 1, characterized in that: The multi-component data acquisition module consists of a physiological parameter monitoring component, a behavior monitoring component, and an environmental perception component. The physiological parameter monitoring component includes a smart wearable device, a mattress-type vital sign monitoring pad, and a portable blood glucose and uric acid detector. The behavior monitoring component includes a millimeter-wave radar, a smart mattress, and a voice emotion recognition device. The environmental perception component includes a temperature and humidity sensor, a gas smoke alarm, and an infrared human motion sensor.

3. The home-based elderly care health monitoring and early warning alarm system as described in claim 1, characterized in that: The health database storage module consists of a structured data layer, an unstructured data layer, and a knowledge graph layer. The structured data layer is used to integrate historical physical examination, medication, and surgical records, and to record physiological parameters, behavioral characteristics, and environmental data. The unstructured data layer is used to record voice emotion logs and video behavior sample libraries. The knowledge graph layer is used to construct a disease risk prediction model and a graph of the linkage between families, communities, and medical institutions.

4. The home-based elderly care health monitoring and early warning alarm system as described in claim 1, characterized in that: The graded early warning module consists of a risk assessment module and a graded triggering module. The risk assessment module is used for dynamic early warning based on LSTM neural network and static assessment based on AHRQ fall risk assessment scale. The graded triggering modules are respectively: Level 1 orange warning for abnormal physiological parameters, Level 2 red warning for abnormal behavior, and Level 3 black warning for environmental crisis.

5. The home-based elderly care health monitoring and early warning alarm system as described in claim 1, characterized in that: The service response execution module consists of a primary response unit and a secondary response unit.

6. The home-based elderly care health monitoring and early warning alarm system as described in claim 5, characterized in that: The primary response unit is the home-based elderly care service center.

7. The home-based elderly care health monitoring and early warning alarm system as described in claim 5, characterized in that: The secondary response units consist of hospitals, social organizations, governments, communities, and families.

8. The home-based elderly care health monitoring and early warning alarm system as described in claim 5, characterized in that: The wireless signal is transmitted by an encrypted wireless base station.

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