An old people home nursing remote monitoring management system

Abstract

This invention discloses a remote monitoring and management system for elderly home care, belonging to the field of intelligent elderly care technology. The system includes a data acquisition layer, a data processing layer, an application service layer, and a terminal interaction layer. The data acquisition layer acquires multi-source data on the elderly's home environment through infrared cameras, environmental sensor arrays, and smart bracelets. The data processing layer uses this multi-source data to perform behavioral safety detection, environmental safety detection, and health analysis and early warning for the elderly at home, identifying falls and prolonged periods of inactivity, and conducting acute and chronic health risk analysis. The application service layer receives instructions from the elderly's caregivers and generates remote reminder signals. The terminal interaction layer outputs remote reminder signals and receives early warning signals, and supports an emergency call function with multi-source data retrieval. This invention achieves multi-dimensional monitoring of the elderly at home, improving the intelligence and safety of home care for the elderly.

Description

Technical Field

[0001] This invention relates to the field of intelligent elderly care technology, specifically to a remote monitoring and management system for home care of the elderly. Background Technology

[0002] As the aging population continues to grow, home-based elderly care has become the mainstream model, and the demand for remote monitoring of elderly home care is becoming increasingly urgent. Various intelligent monitoring systems have been developed accordingly. These systems often integrate basic functions such as image acquisition and environmental sensing, supplemented by simple remote reminders and emergency call capabilities, which to some extent make up for the shortage of manpower for home-based elderly care.

[0003] However, existing monitoring systems still have significant technical shortcomings: multi-source data acquisition lacks coordination; infrared devices are often on 24 / 7, resulting in high power consumption and privacy risks; sensor acquisition frequencies are fixed, limiting data validity; behavioral safety detection and judgment rules are simple and have a high false alarm rate; health analysis uses general indicator thresholds, lacks personalized baselines, and cannot predict chronic health deterioration trends; emergency calls only transmit simple signals without accompanying monitoring data or a multi-contact priority push mechanism, easily causing delays in rescue and failing to meet the refined home monitoring needs of the elderly. Therefore, there is an urgent need for a remote monitoring and management system for elderly home care that integrates multi-source data and has intelligent analysis and remote interaction functions. Summary of the Invention

[0004] The purpose of this invention is to provide a remote monitoring and management system for home care of the elderly, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A remote monitoring and management system for home care of the elderly includes a data acquisition layer, a data processing layer, an application service layer, and a terminal interaction layer; The data acquisition layer is used to acquire multi-source data of elderly people at home, including infrared image data, home environment datasets, and vital sign data. The data processing layer is used to receive and process multi-source data to enable the detection of elderly people's behavior safety at home, the detection of home environment safety, and health analysis and early warning. The application service layer is used to receive instructions input by the guardian and generate remote reminder signals; The terminal interaction layer is used to output remote reminder signals and receive early warning signals, and supports emergency calls.

[0006] Preferably, the data acquisition layer includes an infrared image acquisition module, an environmental sensor group, and a vital signs monitoring module; The infrared image acquisition module collects infrared image data of the elderly person's home through infrared cameras. The infrared cameras are deployed in various activity areas of the elderly person's home, including but not limited to the living room, bedroom, kitchen, bathroom and balcony. Each activity area is equipped with one infrared camera. All infrared cameras are connected to the positioning unit built into the smart bracelet worn by the elderly person. The infrared cameras dynamically activate the corresponding activity area to collect data based on the elderly person's home trajectory points collected by the smart bracelet. When the positioning unit detects that the elderly person has entered a certain activity area, it wakes up the infrared camera in that activity area to start real-time data collection, while the other infrared cameras remain in sleep mode. The environmental sensor group collects a home environment dataset including smoke concentration data, gas concentration data, water leakage monitoring data, and door and window status data. The environmental sensor group consists of smoke sensors, gas sensors, water leakage sensors, and door and window magnetic sensors. The smoke sensors and gas sensors are installed in the kitchen area, the water leakage sensors are installed around the sink and toilet in the bathroom and around the water supply and drainage outlets, and the door and window magnetic sensors are installed at the entrances and exits. All sensors collect data at the same frequency. The vital signs monitoring module collects vital signs data of the elderly by having them wear a smart bracelet. The vital signs data include at least one of heart rate, blood pressure, blood oxygen saturation, and body temperature.

[0007] Preferably, the behavioral safety detection includes fall detection and prolonged static detection, and the specific methods are as follows: Infrared image data is sorted according to acquisition time to obtain an infrared image frame sequence. Each infrared image frame in the infrared image frame sequence is subjected to Gaussian filtering for image denoising and gray-level normalization preprocessing. Then, a pre-trained YOLOv8 target detection model is used to locate and track elderly human targets in each infrared image frame in the infrared image frame sequence. Then, the OpenPose human pose estimation algorithm is used to extract human skeletal key points from the elderly human targets in each infrared image frame in the infrared image frame sequence to obtain the spatiotemporal coordinate information of the continuous frame sequence of human skeletal key points. The human skeletal key points include at least the shoulder, hip, knee, ankle and trunk center, which are strongly related to falls and stationary behavior. The pre-trained YOLOv8 target detection model is obtained by training and optimizing the YOLOv8 target detection model through a home scene infrared human image dataset to adapt to the gray-level features of infrared image frames. The home scene infrared human image dataset is a set of infrared image frames in the home scene of the elderly after Gaussian filtering for denoising and gray-level normalization preprocessing. Based on the spatiotemporal coordinate information of continuous frame sequences of human skeletal key points and preset human abnormal behavior judgment rules, fall detection and long-term stillness detection are performed respectively. When human abnormal behavior that meets the abnormal behavior judgment rules is detected, a corresponding human abnormal behavior behavior safety warning signal is generated. The human abnormal behavior is long-term stillness behavior and fall behavior. The preset rules for determining abnormal human behavior are as follows: When the vertical coordinates of key points of the human skeleton drop sharply within a preset time exceeding a preset threshold, and the human posture angle deviates from the normal standing range by a preset amount, and the behavior characteristics of the elderly lying down and sitting down are excluded, it is judged as a fall. When the displacement of all key points of the human skeleton within a preset time period is less than the preset displacement threshold, and the activity area of ​​the elderly person is not the bedroom area, it is determined to be a long-term static behavior.

[0008] Preferably, the environmental safety analysis module pre-sets corresponding safety thresholds for each sensor. Each sensor compares the real-time collected home environment data with its corresponding safety threshold. When the home environment data exceeds the safety threshold, an environmental safety warning signal corresponding to the home environment data type is generated. Among them, the water leakage sensor uses contact with water to trigger conduction as the judgment threshold, and the door and window magnetic sensor uses abnormal opening of doors and windows within a preset arming period as the judgment threshold. The judgment threshold is the safety threshold.

[0009] Preferably, the method for health analysis is as follows: The basic health data baseline of the elderly is pre-entered and stored. The basic health data baseline is the normal reference range of each vital sign indicator in the vital sign data set in combination with the elderly’s age, gender, past medical history and medication. The vital sign indicators are heart rate, blood pressure, blood oxygen saturation and body temperature. The deviation of vital sign data is compared with the individual's baseline basic health data. When any vital sign indicator in the vital sign data exceeds its corresponding normal reference range for three consecutive times, it is judged as an acute health risk, and a health warning signal corresponding to the acute health risk is generated. Trend fitting analysis is performed on vital sign data collected within a preset statistical period. If the vital sign data continuously deviates from the median of the individual's baseline health data within the preset statistical period, and the deviation shows a continuous increasing trend with the cumulative deviation duration reaching a preset duration threshold, it is determined to be a potential risk of chronic deterioration, and a health warning signal corresponding to the potential risk of chronic deterioration is generated. The preset statistical period is preferably 7 days, the preset duration threshold is that the cumulative deviation duration within the preset statistical period is not less than 4 days, and the daily increase in deviation is not less than 3%. The median of the individual's baseline health data is the median of the normal reference range of each vital sign indicator in the vital sign data.

[0010] Preferably, the terminal interaction layer includes a smart terminal for the elderly and a smart terminal for the guardian. The smart terminal for the elderly is used to output remote reminder signals and has an emergency call function to trigger an emergency call signal. The smart terminal for the guardian is used to receive warning signals including behavioral safety warning signals, health warning signals and environmental safety warning signals, and has a visual interactive interface for the guardian to input instructions and view multi-source data of the elderly at home. The implementation logic of the emergency call function is as follows: when the emergency call function is triggered, multi-source data within a preset time period before the trigger time is retrieved synchronously and pushed to the guardian's smart terminal along with the emergency call signal. The preset time period is preferably 30 seconds. If no response feedback is received from the guardian's smart terminal within the preset waiting time period, the emergency call signal and multi-source data within the preset time period before the trigger time are forwarded to the smart terminals of the corresponding emergency contacts in the preset emergency contact order.

[0011] Preferably, the application service layer includes an instruction receiving module and a reminder generation module. The instruction receiving module is used to receive instructions input by the guardian through the guardian's smart terminal. The instructions include medication reminders, diet reminders, and activity reminders. The content of the instructions includes the reminder time, reminder type, reminder content, and repetition cycle. The reminder generation module is used to generate corresponding remote reminder signals according to instructions. Specifically, the reminder generation module matches and generates a remote reminder signal of the corresponding modality according to the reminder time and reminder type input by the guardian, and sends it to the elderly's smart terminal at a preset reminder time. The modality of the remote reminder signal includes at least one of voice reminder, vibration reminder and light reminder. For medication reminders, a composite reminder modality of voice reminder and vibration reminder or light reminder is used. For routine rest reminders, voice reminder modality is preferred.

[0012] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: The data acquisition layer of this invention dynamically starts and stops the infrared camera according to the elderly person's trajectory points, balancing low power consumption and privacy protection, and improving the synergy and effectiveness of multi-source data acquisition. The data processing layer relies on target detection and human posture estimation algorithms to achieve accurate detection of abnormal behavior, significantly reducing the false alarm rate. At the same time, it constructs a personalized basic health data baseline for the elderly, enabling real-time assessment of acute risks of vital signs and early prediction of chronic deterioration trends, improving the pertinence and accuracy of health analysis. The emergency call function of the terminal interaction layer can simultaneously retrieve multi-source monitoring data before triggering, and adds an emergency contact priority push mechanism to avoid delays in rescue. Overall, it greatly improves the intelligence and refinement level of the remote monitoring and management system for elderly home care, and can better meet the actual home monitoring needs. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the system functional modules of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This embodiment provides a remote monitoring and management system for elderly home care, applied to intelligent home-based elderly care monitoring scenarios. The system comprises a data acquisition layer, a data processing layer, an application service layer, and a terminal interaction layer. These layers are interconnected and share data, enabling multi-dimensional and intelligent remote monitoring and management of the elderly's home status through collaborative work. The hardware carriers involved in each layer utilize embedded industrial control computers and edge computing gateways as processing terminals, and are coupled with various sensing devices and smart terminals to complete data acquisition and interaction. The overall system architecture is as follows: Figure 1 As shown.

[0017] The data acquisition layer serves as the data source for the system, acquiring infrared image data, home environment datasets, and vital sign data of the elderly in their homes. This layer includes an infrared image acquisition module, an environmental sensor group, and a vital sign monitoring module. The infrared image acquisition module uses an infrared thermal imaging camera with a resolution of 320×240 as the acquisition terminal. Each infrared camera is deployed in the core activity areas of the elderly in their homes, such as the living room, bedroom, kitchen, bathroom, and balcony, with one camera deployed in each area. All infrared cameras are wirelessly connected to the built-in Bluetooth positioning unit of the smart bracelet worn by the elderly. The system can dynamically activate the infrared camera in the corresponding activity area for real-time acquisition based on the home trajectory points collected by the smart bracelet, while the cameras in other areas remain in a low-power sleep state. The environmental sensor group consists of smoke sensors, gas sensors, water leakage sensors, and door and window magnetic sensors. Each sensor is connected to the processing terminal. The smoke and gas sensors are placed near the kitchen stove, the water leakage sensors are placed around the bathroom sink and toilet, and at the water supply and drainage outlets on the balcony, and the door and window magnetic sensors are placed at the corresponding positions on the door frame and door leaf of the entrance door and balcony floor-to-ceiling window. The data collected forms a home environment dataset containing smoke concentration data, gas concentration data, water leakage monitoring data, and door and window status data. The vital signs monitoring module uses a smart bracelet worn by the elderly as the data collection terminal. This smart bracelet integrates heart rate, blood pressure, blood oxygen, and body temperature sensors to collect vital signs data including heart rate, blood pressure, blood oxygen saturation, and body temperature in real time, and wirelessly transmits the data to the processing terminal via Bluetooth 5.0.

[0018] The data processing layer, the core analysis layer of the system, is mounted in the edge computing gateway. It receives and processes multi-source data uploaded from the data acquisition layer to achieve behavioral safety detection, environmental safety detection, and health analysis and early warning. This layer includes a behavioral safety detection module, an environmental safety analysis module, and a health early warning module. The behavioral safety detection module is used to detect falls and prolonged static states in the elderly. First, the infrared image data is sorted by acquisition timestamp to form an infrared image frame sequence. After Gaussian filtering for noise reduction and grayscale normalization preprocessing, it is input into a pre-trained YOLOv8 target detection model trained and optimized from a home scene infrared human image dataset to locate and track elderly human targets and eliminate them. Background frames lacking valid elderly human targets are used. The OpenPose human pose estimation algorithm is then employed to extract skeletal key points at the center of the shoulder, hip, knee, ankle, and torso, yielding a continuous frame sequence of spatiotemporal coordinates for these key points. Finally, detection is performed based on this spatiotemporal coordinate information and pre-defined abnormal human behavior judgment rules. A fall is identified when the vertical coordinate of a skeletal key point drops by more than 50cm within 2 seconds, the posture angle deviates from the normal standing range by more than 60°, and normal lying or sitting behavior is excluded. Prolonged stillness is identified when the displacement of all skeletal key points is less than 5cm within 10 minutes and the elderly person is not in a bedroom area. The system detects abnormal behavior and generates corresponding safety warning signals. The environmental safety analysis module pre-sets safety thresholds for each sensor: smoke sensor threshold is ≤0.15%obs / m³, gas sensor threshold is ≤5%LEL, water leakage sensor threshold is triggered by contact with water, and door / window magnetic sensor threshold is abnormal opening of doors and windows during the deployment period from 22:00 to 6:00 the next day. Each sensor collects data in real time and compares it with the corresponding safety threshold; if the data exceeds the safety threshold, a corresponding environmental safety warning signal is generated. The health warning module pre-records and stores the individual's basic health information, set based on the elderly person's age, gender, medical history, and medication. The baseline health data is defined as the normal reference range for vital signs such as heart rate, blood pressure, blood oxygen saturation, and body temperature. Real-time vital sign data is then compared with this baseline health data. If any vital sign exceeds the corresponding normal reference range three times consecutively, it is considered an acute health risk and a corresponding health warning signal is generated. At the same time, trend fitting analysis is performed on the vital sign data with a preset statistical period of 7 days. When a vital sign continuously deviates from the median of the baseline health data, the deviation shows a continuous increasing trend of not less than 3%, and the cumulative deviation duration is not less than 4 days, it is considered a potential risk of chronic deterioration and a corresponding health warning signal is generated.

[0019] The application service layer is installed in the system software of the processing end. It is used to receive instructions input by the guardian and generate remote reminder signals. This layer includes an instruction receiving module and a reminder generation module. The instruction receiving module receives medication reminders, diet reminders, and activity reminders input by the guardian through the visual interactive interface of the guardian's smart terminal, and stores the instructions after parsing them. The reminder generation module generates a remote reminder signal of the corresponding mode according to the parsed instructions, matching the reminder time and reminder type, and accurately sends it to the elderly's smart terminal according to the preset reminder time. For medication reminders, a composite reminder mode of voice reminder + vibration reminder is used. The preset medication reminder content is played by voice and the vibration lasts for 10 seconds. For routine reminders such as diet and activity, the voice reminder mode is used first.

[0020] The terminal interaction layer serves as the human-computer interaction end of the system, comprising smart terminals for the elderly and smart terminals for guardians. The smart terminals for the elderly utilize smart bedside units and smart bracelets with voice playback, vibration, and light alerts. They can receive remote reminder signals from the application service layer and output corresponding reminders accordingly. They also feature a physical emergency call button, allowing for one-click triggering of an emergency call and the generation of an emergency call signal. The smart terminals for guardians utilize mobile devices such as smartphones and tablets equipped with a dedicated monitoring and management app. Their visual interface receives three types of warning signals generated by the data processing layer: behavioral safety, environmental safety, and health alerts. These warning signals are displayed in a pop-up format. The system provides both window and sound alerts to guardians, while also allowing them to view multi-source data on the elderly person's home in real time. It also provides an input interface for guardians to edit, modify, and delete various commands. Once the emergency call function is triggered, it immediately retrieves multi-source data from the 30 seconds prior to the trigger and pushes it along with the emergency call signal to the guardian's smart terminal. If no confirmation response is received from the guardian within a preset 5-minute waiting period, the emergency call signal and retrieved multi-source data are forwarded sequentially to the smart terminals of the guardian's preset emergency contacts, such as children, community grid workers, and medical personnel, until a response is received.

[0021] The overall workflow of the remote monitoring and management system for elderly home care of the present invention is as follows: The data acquisition layer uses infrared cameras, environmental sensor groups, and smart bracelets to dynamically collect multi-source data of the elderly at home in real time, and uploads the multi-source data to the data processing layer; The data processing layer preprocesses and intelligently analyzes the multi-source data, completes behavioral safety and environmental safety detection and health risk analysis, generates various early warning signals and pushes them to the guardian's smart terminal; The guardian inputs various commands through the guardian's smart terminal, which are parsed by the application service layer and generate corresponding remote reminder signals, which are sent to the elderly's smart terminal to complete the reminder output; When the elderly trigger the emergency call function, the system simultaneously retrieves historical multi-source data and pushes it to the guardian and emergency contact, thereby realizing the whole process of remote monitoring and management of elderly home care.

[0022] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A remote monitoring and management system for home care of the elderly, characterized in that, include: The data acquisition layer is used to acquire multi-source data of elderly people at home, including infrared image data, home environment datasets, and vital sign data. The data processing layer is used to receive and process multi-source data to enable the detection of elderly people's behavior safety at home, the detection of home environment safety, and health analysis and early warning. The application service layer is used to receive instructions input by the guardian and generate remote reminder signals; The terminal interaction layer is used to output remote reminder signals and receive early warning signals, and supports emergency calls.

2. The remote monitoring and management system for home care of the elderly according to claim 1, characterized in that, The data acquisition layer includes: An infrared image acquisition module is used to collect infrared image data of elderly people at home through infrared cameras. The infrared cameras are respectively deployed in various activity areas of the elderly people at home, and the infrared cameras in the corresponding activity areas are dynamically activated to collect data based on the home trajectory points of the elderly people collected by the smart bracelet. An environmental sensor array is used to collect home environment datasets, which include smoke concentration data, gas concentration data, water leakage monitoring data, and door and window status data. The vital signs monitoring module is used to collect vital signs data of elderly people by having them wear a smart bracelet. The vital signs data include at least one of heart rate, blood pressure, blood oxygen saturation, and body temperature.

3. The remote monitoring and management system for elderly home care according to claim 2, characterized in that, The data processing layer includes: The behavioral safety detection module is used to detect the behavior of the elderly based on infrared image data and generate behavioral safety warning signals. The behavioral safety detection includes fall detection and long-term static detection. The environmental safety analysis module is used to perform home environment safety detection based on home environment datasets and generate environmental safety early warning signals; The health early warning module is used to perform health analysis based on vital sign data and generate health early warning signals based on the analysis results.

4. The remote monitoring and management system for elderly home care according to claim 3, characterized in that, The method for behavioral safety detection is as follows: Infrared image data are sorted according to acquisition time to obtain infrared image frame sequence. After image denoising and normalization preprocessing of infrared image frame sequence, the elderly human target in each infrared image frame in infrared image frame sequence is located and tracked by pre-trained target detection model. Then, human pose estimation algorithm is used to extract human skeleton key points of elderly human target in each infrared image frame in infrared image frame sequence to obtain spatiotemporal coordinate information of continuous frame sequence of human skeleton key points. Based on the spatiotemporal coordinate information of continuous frame sequences of key points of the human skeleton and the preset rules for judging abnormal human behavior, fall detection and long-term static detection are performed respectively. When abnormal human behavior that meets the rules for judging abnormal behavior is detected, a corresponding behavior safety warning signal is generated.

5. The remote monitoring and management system for elderly home care according to claim 3, characterized in that, The method for the health analysis is as follows: The basic health data baseline of the elderly is pre-entered and stored. The basic health data baseline is the normal reference range of each vital sign indicator in the vital sign data set in combination with the elderly’s age, gender, past medical history and medication. The deviation of vital sign data is compared with the individual's baseline basic health data. When any vital sign indicator in the vital sign data exceeds its corresponding normal reference range for three consecutive times, it is judged as an acute health risk, and a health warning signal corresponding to the acute health risk is generated. Trend fitting analysis is performed on the vital signs data collected within the preset statistical period. If the vital signs data continuously deviate from the median of the individual's basic health data baseline within the preset statistical period, and the deviation shows a continuous increasing trend and the cumulative deviation time reaches the preset time threshold, it is determined to be a potential risk of chronic deterioration, and a health warning signal corresponding to the potential risk of chronic deterioration is generated.

6. The remote monitoring and management system for elderly home care according to claim 3, characterized in that, The terminal interaction layer includes: The smart terminal for the elderly is used to output remote reminder signals and has an emergency call function to trigger an emergency call signal; The guardian's smart terminal is used to receive warning signals, including behavioral safety warning signals, health warning signals, and environmental safety warning signals, and has a visual interactive interface for guardians to input commands.

7. The remote monitoring and management system for elderly home care according to claim 6, characterized in that, The application service layer includes: The instruction receiving module is used to receive instructions from the guardian, including medication reminders, diet reminders, and activity reminders. The reminder generation module is used to generate corresponding remote reminder signals based on instructions.

8. The remote monitoring and management system for home care of the elderly according to claim 7, characterized in that, The reminder generation module matches and generates a remote reminder signal of the corresponding modality based on the reminder time and reminder type input by the guardian, and sends it to the elderly's smart terminal at a preset reminder time; the modality of the remote reminder signal includes at least one of voice reminder, vibration reminder and light reminder.

9. The remote monitoring and management system for home care of the elderly according to claim 6, characterized in that, When the emergency call function is triggered, multi-source data within a preset time period before the trigger time is retrieved simultaneously and pushed to the guardian's smart terminal along with the emergency call signal. If no response is received from the guardian's smart terminal within the preset waiting time, the emergency call signal will be forwarded to the smart terminal of the next highest-ranking emergency contact in the preset order, and multi-source data within the preset time before the trigger time will be retrieved simultaneously.

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