Intelligent nursing and monitoring system for health care

Abstract

The invention discloses an intelligent nursing monitoring system for health care, and belongs to the technical field of intelligent nursing. The intelligent nursing monitoring system for health care comprises a monitoring module, an analysis module, an early warning module and a mobile terminal. According to the system, the problems that in the prior art, comprehensive and intelligent monitoring cannot be carried out on the dangerous condition of a household nursed object, and then all-weather health condition analysis and alarm cannot be carried out are solved, and the AI camera and the millimeter wave radar are used for carrying out real-time data acquisition; any expression change or limb movement of the nursed object can be captured in time, health problems can be found and processed in time, the safety of the nursed object is guaranteed, and an alarm signal is sent out in time when it is monitored that the health state of the nursed object is abnormal. It is ensured that family members or nursing personnel of the nursed object can rapidly receive the abnormal information and take corresponding countermeasures, and the safety and health of the nursed object are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent nursing technology, and in particular to a health care intelligent nursing monitoring system. Background Art

[0002] The proportion of elderly care recipients living alone in the society is increasing year by year. People are paying more and more attention to the monitoring of the physical and mental health of care recipients who are alone at home. Timely detection and even warning of dangerous physical and mental conditions of care recipients at home have very great social significance and market value.

[0003] The Chinese patent with publication number CN201320852485.8 discloses a care system for a cared person based on the Internet of Things. The system is based on RFID personnel positioning; posture detection, behavior detection, and smoke detection for videos of cared person's activity scenes; call for help detection for cared person's activity scenes; multi-mode identity authentication integrating biometrics such as face, finger vein, and fingerprint; personal health detection and environmental safety detection based on multi-sensor state perception; and information release based on visual terminals. The system can be used to realize intelligent detection and alarm of events such as the location, fall, coma, fight, and call for help of the cared person, and has home environment safety detection, personal health condition detection, highly acceptable personal identity identification and authentication, video and graphic information release, etc., to provide comprehensive care services for the cared person.

[0004] In actual use, the above patent cannot achieve comprehensive intelligent monitoring of dangerous conditions of the people being cared for at home, and thus cannot perform health status analysis and alarms around the clock; therefore, it does not meet existing needs. In response to this, we have proposed a health care intelligent care monitoring system. Summary of the invention

[0005] The purpose of the present invention is to provide a health care intelligent nursing monitoring system, which uses AI cameras and millimeter wave radars to collect real-time data, so that any changes in facial expressions or body movements of the cared object can be captured instantly, so that health problems can be discovered and dealt with in time to ensure the safety of the cared object. When the health status of the cared object is monitored to be abnormal, an alarm signal is issued in time to ensure that the family members or caregivers of the cared object can quickly receive abnormal information and take corresponding countermeasures, effectively ensuring the safety and health of the cared object and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a health care intelligent nursing monitoring system, comprising: A monitoring module, which is used to capture the facial video and scene images of the object under care by using monitoring devices, monitor the physical state of the object under care in real time, and collect monitoring data in real time. The monitoring devices include an AI camera, a millimeter-wave radar, and an environmental sensor; An analysis module, which is used to analyze the environment where the object under care is located, the facial expression features of the object under care; and analyze the breathing and heart rate parameters of the object under care.

[0007] An early warning module, which is used to judge the living environment, health status, and behavior status of the object under care, send an alarm signal according to the judgment result, and encrypt the data; A mobile terminal, which is used to realize remote monitoring and management, adjust the monitoring angle and range of the AI camera, and provide a voice intercom function.

[0008] Preferably, the monitoring module includes: An environmental monitoring module, which is used to capture the facial video and scene images of the object under care by using monitoring devices, and process and extract features from the scene images; A health monitoring module, which is used to use the millimeter-wave radar to monitor the physical state of the object under care in real time and collect monitoring data in real time; A data uploading module, which is used to upload the monitoring data of the environmental monitoring module and the monitoring data of the environmental monitoring module to the analysis module.

[0009] Preferably, the health monitoring module specifically includes: Using the millimeter-wave radar to emit electromagnetic waves to capture the signal reflected by the object under care in the path, and removing the noise and clutter in the reflected signal, including removing the DC component and amplitude normalization; Using the fast Fourier transform for human body positioning, adopting the MTI algorithm to suppress the static target echo, and obtaining the tiny motion signal caused by the breathing and heartbeat of the chest cavity of the object under care, which is the breathing rate of the object under care; Performing an arctangent operation on the reflected signal, extracting the phase information, and processing the jump phenomenon of the phase information to obtain a continuous phase curve; Performing a difference operation on the processed phase curve, and extracting the frequency range where the breathing and heartbeat signals are located in the phase curve, Extracting the envelope of the processed phase curve and performing normalization processing to obtain the final heartbeat waveform, and obtaining the heart rate of the object under care through the extracted heartbeat waveform; Using a smart bracelet to collect the blood pressure, blood oxygen, and body temperature data of the object under care in real time, assisting in management, relieving emotions, and warning of mental health status.

[0010] Preferably, the environmental monitoring module includes: Configure the monitoring area of the AI camera and the monitoring parameters of the environmental sensors according to the care needs; Use the AI camera to continuously capture the facial video and scene images of the cared-for object at a specific frame rate, and capture the subtle facial expression changes of the cared-for object in the facial video; Denoise, grayscale, and enhance the collected scene images, and extract features from the processed images; Use environmental sensors to continuously monitor the temperature, humidity, and air quality parameters of the living environment. The environmental sensors include temperature and humidity sensors, smoke sensors, noise sensors, and combustible gas detectors.

[0011] Preferably, the extracting features from the processed images specifically includes: Use computer vision technology to detect and track human faces in the video, and extract the face images of each frame; Extract expression features from the face images and analyze the extracted expression features.

[0012] Preferably, the analysis module includes: A data receiving module for receiving the environmental monitoring data and health monitoring data of the cared-for object collected by the monitoring module; An environmental analysis module for analyzing the environment where the cared-for object is located and analyzing the facial expression features of the cared-for object according to the monitored environmental monitoring data; A health analysis module for analyzing the breathing and heart rate parameters of the cared-for object, and calculating the real-time average speed and real-time horizontal distance change amount of the cared-for object in the height and horizontal directions.

[0013] Preferably, the environmental analysis module includes: Analyze the air quality and noise level of the living environment of the cared-for object using the monitoring parameters of the environmental sensors; Use image recognition technology to analyze the images captured by the AI camera to identify the physical characteristics of the living environment of the cared-for object.

[0014] Preferably, the health analysis module includes: Analyze the skin dilation movement of the cared-for object caused by breathing and heartbeat using the breathing rate and heart rate of the cared-for object, and invert the breathing and heart rate parameters based on the skin dilation movement caused by breathing and heartbeat; Analyze the frequency, amplitude, and phase parameters of the millimeter-wave radar reflection signal, and calculate the position, speed, and acceleration of the cared-for object according to the frequency, amplitude, and phase parameters of the millimeter-wave radar reflection signal; Perform mean filtering and piecewise linear fitting on the calculated data to obtain the approximate motion law of the cared-for object; Analyze the approximate motion law to obtain the real-time average velocity and the change amount of the real-time horizontal distance of the object under care in the height and horizontal directions.

[0015] Preferably, the warning module includes: A judgment module, configured to judge the living environment of the object under care, the health condition of the object under care, and the situation of the object under care falling or getting lost; An alarm module, configured to send the judgment result of the judgment module to the terminal of the family member or caregiver of the object under care, and send a corresponding alarm signal according to the judgment result; A security protection module, configured to encrypt the received monitoring data and judgment result, identify and allocate permissions to the access user identity, and identify the access user identity during the data access process.

[0016] Preferably, the judgment module includes: Set thresholds for the average velocity and the change amount of the horizontal distance of the object under care in the height and horizontal directions; Compare the real-time average velocity and the change amount of the real-time horizontal distance of the object under care in the height and horizontal directions with the preset thresholds to judge whether the object under care has fallen; Use the air quality to judge whether the living environment has an impact on the respiratory system of the object under care and judge whether there are potential safety hazards such as gas leakage, open fire, and long-term smoking in the living environment of the object under care; Use the noise level to judge whether the living environment has an impact on the sleep quality of the object under care; Judge whether the sleep duration, the number of turns, the number of apnea times, and the breathing frequency of the object under care are abnormal according to the breathing and heart rate parameters of the object under care; Use the facial expression features of the object under care to judge the emotional state of the object under care, and perform fusion analysis on the emotional state of the object under care and the breathing and heart rate parameters of the object under care to judge the mental state and health risks of the object under care.

[0017] Compared with the prior art, the beneficial effects of the present invention are: The present invention can achieve all-round monitoring of the facial expressions and body movements of the cared-for object, can more accurately evaluate the health status of the cared-for object, and timely detect potential health problems. By using an AI camera and a millimeter-wave radar for real-time data collection, any expression change or body movement of the cared-for object can be instantly captured, so as to be able to timely discover and handle health problems. Especially in case of emergency, it can quickly respond to ensure the safety of the cared-for object. When the abnormal health status of the cared-for object is detected, through multiple alarm methods such as APP, text message, and phone call, the family members or caregivers of the cared-for object are notified in a timely manner, ensuring that the family members or caregivers of the cared-for object can quickly receive the abnormal information and take corresponding countermeasures, effectively ensuring the safety and health of the cared-for object. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the modules of the intelligent care and monitoring system for health preservation of the present invention; Figure 2 It is a schematic diagram of the process of the intelligent care and monitoring system for health preservation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] In order to solve the problem that in the actual use process of the prior art, it is impossible to comprehensively and intelligently monitor the dangerous situation of the cared-for object at home, and thus it is impossible to conduct all-weather health status analysis and alarm, please refer to Figure 1 - Figure 2 , the following technical solutions are provided in this embodiment An intelligent care and monitoring system for health preservation, comprising: A monitoring module, configured to use monitoring devices to capture the facial video and scene images of the cared-for object, monitor the physical state of the cared-for object in real time, and collect monitoring data in real time. The monitoring devices include an AI camera, a millimeter-wave radar, and an environmental sensor; An analysis module, configured to analyze the environment where the cared-for object is located, the facial expression features of the cared-for object; and analyze the respiration and heart rate parameters of the cared-for object.

[0021] An early warning module, configured to judge the living environment, health status, and behavior state of the cared-for object, send an alarm signal according to the judgment result, and encrypt the data; A mobile terminal is used to achieve remote monitoring and management, adjust the monitoring angle and range of an AI camera to ensure blind - spot - free monitoring, and provide a voice intercom function to facilitate remote communication between the family members or caregivers of the cared - for object and the cared - for object.

[0022] The monitoring module includes: The environmental monitoring module is used to capture the facial video and scene images of the cared - for object using monitoring devices, and process and extract features from the scene images; The cared - for object is monitored in real - time using an AI camera. The home situation of the cared - for object can be viewed in real - time through an APP. For example, sudden situations such as the cared - for object falling, tripping, or being unable to get up can be monitored; face images are taken regularly for risk prediction of cerebral infarction or myocardial infarction; the activity patterns of people can be monitored; at the same time, it has an alarm function, and comprehensive analysis is performed based on the collected data, which can effectively monitor and interact with the cared - for objects living alone and those in need of care. Using facial expression and motion recognition, it can monitor in time and give alarms and requests for help.

[0023] The health monitoring module is used to monitor the physical state of the cared - for object in real - time using a millimeter - wave radar, and collect monitoring data in real - time. Monitoring using a millimeter - wave radar can achieve the judgment of whether there is a person and the extraction of vital signs such as breathing and heartbeat, such as time in bed, heart rate output, etc.; The data upload module is used to upload the monitoring data of the environmental monitoring module and the monitoring data of the environmental monitoring module to the analysis module.

[0024] The health monitoring module specifically includes: The millimeter - wave radar is used to emit electromagnetic waves to capture the signals reflected by the cared - for object in the path, and remove the noise and clutter in the reflected signals, including removing the DC component and amplitude normalization; Fast Fourier Transform is used for human body positioning, and the MTI algorithm is used to suppress the echo of static targets to obtain the tiny motion signals caused by the breathing and heartbeat of the cared - for object's chest, which is the breathing rate of the cared - for object; The arctangent operation is performed on the reflected signal to extract the phase information, and the jump phenomenon of the phase information is processed to obtain a continuous phase curve; Differential processing is performed on the processed phase curve, including signal enhancement and noise suppression. A sliding - average filter is used to smooth the differential phase data to remove high - frequency noise, and the frequency range where the breathing and heartbeat signals are located in the phase curve is extracted. The breathing rate is usually between 0.1 - 0.5Hz, and the heart rate is usually between 0.8 - 3Hz. The envelope of the processed phase curve is extracted and normalized to obtain the final heartbeat waveform. Through the extracted heartbeat waveform, the heart rate of the cared - for object is obtained; Use a smart bracelet to collect the blood pressure, blood oxygen and body temperature data of the cared-for object in real time, assist in management, relieve emotions, and warn of mental health status.

[0025] Environmental monitoring module, including: Configure the monitoring area of the AI camera and the monitoring parameters of the environmental sensor according to the care needs, such as setting the boundary of the monitoring area, adjusting the sensitivity of the sensor, etc.; Use the AI camera to continuously capture the facial video and scene images of the cared-for object at a specific frame rate, and capture the subtle facial expression changes of the cared-for object in the facial video; Denoise, grayscale and image enhancement processing are performed on the collected scene images to improve the image quality and highlight key information, and feature extraction is performed on the processed images; Use environmental sensors to monitor the temperature, humidity and air quality parameters of the living environment in real time. The environmental sensors include temperature and humidity sensors, smoke sensors, noise sensors and combustible gas detectors.

[0026] Feature extraction is performed on the processed images, specifically including: Use computer vision technology to perform face detection and tracking on the video, and extract the face image of each frame; Extract expression features from the face image, such as the movements of facial expression muscles, the opening and closing degree of the eyes, the shape of the lips, etc., and analyze the extracted expression features.

[0027] Analysis module, including: Data receiving module, used to receive the environmental monitoring data and health monitoring data of the cared-for object collected by the monitoring module; Environmental analysis module, used to analyze the environment where the cared-for object is located according to the monitored environmental monitoring data and analyze the facial expression features of the cared-for object. The state set includes that the cared-for object is not in the picture, the cared-for object is lying flat, the cared-for object is standing or walking, the cared-for object is squatting or sitting still, and other states; Health analysis module, used to analyze the breathing and heart rate parameters of the cared-for object, and calculate the real-time average speed and real-time horizontal distance change amount of the cared-for object in the height and horizontal directions.

[0028] Environmental analysis module, including: Use the monitoring parameters of the environmental sensor to analyze the air quality and noise level of the living environment of the cared-for object; Use image recognition technology to analyze the images captured by the AI camera, identify the physical characteristics of the living environment of the cared-for person, such as furniture layout, room size, window position, and whether there is water on the floor or the faucet is not turned off. This can prevent the cared-for person from slipping and water from overflowing. In addition, image recognition can also detect abnormal situations in the living environment, such as items being placed randomly and potential safety hazards, and thus issue early warnings in a timely manner. The alarm methods can include client warnings, pop-up prompts, voice notifications, WeChat push, etc., to ensure that relevant personnel can receive and respond to the alarm information in a timely manner; Real-time collect environmental data through the AI camera. These data include but are not limited to air quality, light intensity, noise level, temperature, humidity, etc. Using image recognition technology to analyze the images captured by the camera can identify the physical characteristics of the living environment, such as furniture layout, room size, window position, etc. In addition, image recognition can also detect abnormal situations in the living environment, such as items being placed randomly and potential safety hazards, and thus issue early warnings in a timely manner. Combine the real-time monitored environmental data and the results of image recognition for comprehensive analysis. According to the analysis results, put forward improvement suggestions or take measures. If it is found that there are problems in the living environment that are not conducive to health or comfort, the environmental settings can be adjusted in a timely manner, such as increasing green plants, improving ventilation, reducing noise, etc. When the environmental sensor detects abnormal situations in the living environment, such as too high temperature, too high humidity, or poor air quality, it will also issue an alarm to remind the family members or caregivers of the cared-for person to adjust the environmental parameters in a timely manner to ensure the living comfort of the cared-for person. The family members or caregivers of the cared-for person can view the surveillance videos and living environment data through multiple terminals such as mobile phones, tablets, and computers to achieve remote monitoring and management. Through the pan-tilt control function, the family members or caregivers of the cared-for person can adjust the monitoring angle and range of the camera to ensure blind spot-free monitoring. At the same time, the system also supports the voice intercom function, which is convenient for the family members or caregivers of the cared-for person to communicate with the cared-for person remotely.

[0029] Health analysis module, including: Analyze the skin dilation movement of the cared-for person caused by breathing and heartbeat using the breathing rate and heart rate of the cared-for person, and invert the breathing and heart rate parameters based on the skin dilation movement caused by breathing and heartbeat. This monitoring method is not only accurate but also not affected by factors such as environmental temperature and light, and has higher reliability and stability; Analyze the frequency, amplitude, and phase parameters of the millimeter-wave radar reflection signal, and calculate the position, speed, and acceleration of the cared-for person based on the frequency, amplitude, and phase parameters of the millimeter-wave radar reflection signal; Perform mean filtering and piecewise linear fitting on the calculated data to obtain the approximate motion law of the cared-for person; Analyze the approximate motion law to obtain the real-time average speed and the change in real-time horizontal distance of the cared-for object in the height and horizontal directions.

[0030] Early warning module, including: Judgment module, used to judge the living environment of the cared-for object, the health status of the cared-for object, and the situation of the cared-for object falling or getting lost; Alarm module, used to send the judgment result of the judgment module to the terminal of the family member or caregiver of the cared-for object, and send corresponding alarm signals according to the judgment result; Security protection module, used to encrypt the received monitoring data and judgment results, identify the access user identity and allocate permissions, and identify the access user identity during the data access process.

[0031] Judgment module, including: Preset the thresholds of the average speed and the change in horizontal distance of the cared-for object in the height and horizontal directions; Compare the real-time average speed and the change in real-time horizontal distance of the cared-for object in the height and horizontal directions with the preset thresholds to judge whether the cared-for object has fallen; If the judgment result is that the average speed and the change in horizontal distance of the cared-for object in the height and horizontal directions exceed the set thresholds and stay on the ground briefly, then it is judged as a fall behavior; Use air quality to judge whether the living environment has an impact on the respiratory system of the cared-for object and whether there are potential safety hazards such as gas leakage, open fire, and long-term smoking in the living environment of the cared-for object; Use the noise level to judge whether the living environment has an impact on the sleep quality of the cared-for object, such as judging whether the activity state of the cared-for object is normal and whether the living environment is suitable, etc.; Judge whether the sleep duration, the number of turns, the number of apnea times, and the breathing frequency of the cared-for object are abnormal according to the breathing and heart rate parameters of the cared-for object, such as sudden increase or decrease in heart rate, irregular breathing; Use the facial expression features of the cared-for object to judge the emotional state of the cared-for object. If emotions such as pain and anxiety appear at the same time, it can further improve the accuracy of early warning of health risks.

[0032] Fusion analyze the emotional state of the cared-for object and the breathing and heart rate parameters of the cared-for object to judge the mental state and health risks of the cared-for object.

[0033] Millimeter-wave radar has the advantages of high precision, strong penetration, and being unaffected by light and water vapor. Therefore, it is suitable for fall detection in various environments. Through a high-density millimeter-wave antenna array and DSP signal processing technology, the accuracy and reliability of fall detection can be further improved. Millimeter-wave radar technology can analyze the behavior postures of the human body in real time, solving the privacy protection problem in areas such as bathroom falls and bedroom falls. When a person falls, there are obvious differences in the instantaneous speed and position parameters relative to the millimeter-wave radar compared to other actions. The millimeter-wave radar can detect and construct a set of characteristic parameters in real time through pattern recognition methods to achieve fall detection. Once a fall is detected, the millimeter-wave radar can immediately notify the family members or caregivers of the cared-for object in the community so that rescue measures can be taken in a timely manner.

[0034] An alarm module, including: Using an AI camera to capture the facial video of the cared-for object and capture the subtle changes in the facial expressions of the cared-for object; Using a millimeter-wave radar to monitor the actions of the cared-for object, including vital sign information such as breathing, heart rate, and body movement; Using computer vision technology to perform face detection and tracking on the video and extract the face image of each frame; Extract expression features from the face image, such as the movements of facial expression muscles, the degree of eye opening and closing, the shape of the lips, etc., and analyze the extracted expression features to judge the emotional state of the cared-for object; Using a millimeter-wave radar to monitor the breathing rate, heart rate, and body movement of the cared-for object in real time; Analyze the breathing rate, heart rate, and body movement of the cared-for object, such as a sudden increase or decrease in heart rate, irregular breathing, etc., which may be precursors of myocardial infarction or cerebral infarction; When the system detects an abnormal emotional state or vital signs, immediately trigger an early warning mechanism, and formulate corresponding intervention measures according to the specific situation of the cared-for object, such as providing psychological support, adjusting the drug dosage, or seeking emergency medical treatment.

[0035] Judge the sleep duration, the number of turns, the number of apnea times, and whether the breathing rate is abnormal of the cared-for object based on the breathing and heart rate parameters of the cared-for object. By detecting the micro-movements on the surface of the cared-for object in a non-contact manner, the millimeter-wave radar can judge whether the sleep duration, the number of turns, the number of apnea times, and the breathing rate are abnormal, etc., which helps to comprehensively evaluate the current sleep state and sleep quality of the cared-for object, as well as whether there are physiological abnormalities. The millimeter-wave radar can continuously track the activity situation of the cared-for object, including its motion state, position information, and trajectory, etc., and can understand the daily activity habits of the cared-for object, and timely discover abnormal behaviors or activity patterns, so as to evaluate its health status.

[0036] Working principle: When using the intelligent care and monitoring system for health preservation of the present invention, according to Figure 1 and Figure 2 , the following steps are included: Step 1: Use a millimeter-wave radar for human body positioning, monitor the breathing frequency and heart rate of the cared-for object, and use an intelligent bracelet to collect the blood pressure, blood oxygen, and body temperature data of the cared-for object in real time, assist in management, relieve emotions, and warn of mental health conditions Step 2: Use an AI camera to capture the facial video and scene images of the cared-for object, extract features from the processed images, and use an environmental sensor to monitor the temperature, humidity, and air quality parameters of the living environment in real time; Step 3: Analyze the air quality and noise level of the living environment of the cared-for object using the monitoring parameters of the environmental sensor, and identify the physical characteristics of the living environment of the cared-for object; Step 4: Analyze the breathing and heart rate parameters of the cared-for object, calculate the position, speed, and acceleration of the cared-for object, and obtain the real-time average speed and real-time horizontal distance change amount of the cared-for object in the height and horizontal directions based on the position, speed, and acceleration of the cared-for object; Step 5: Compare the real-time average speed and real-time horizontal distance change amount of the cared-for object in the height and horizontal directions, determine whether the cared-for object has fallen, and determine whether there is an abnormality in the living environment based on the environmental monitoring results; Step 6: Judge whether the sleep duration, turning times, apnea times, and breathing frequency of the cared-for object are abnormal based on the breathing and heart rate parameters of the cared-for object, judge the emotional state of the cared-for object using the facial expression features of the cared-for object, and perform fusion analysis on the emotional state of the cared-for object and the breathing and heart rate parameters of the cared-for object to judge the mental state and health risks of the cared-for object Step 7: If the cared-for object has fallen, there is an abnormality in the living environment, or there is an abnormality in the emotional state and health state of the cared-for object, an alarm signal is issued.

[0037] In summary, for the intelligent healthcare monitoring system of the present invention, the millimeter-wave radar adopts a non-contact detection method, which will not cause any harm to the object being cared for, and at the same time avoids the problem of privacy infringement. It is particularly suitable for scenarios with high privacy requirements. The millimeter-wave radar has the ability to detect multiple targets, can monitor the states of multiple objects being cared for simultaneously, distinguish the motion states and position information of different targets, and combine with the data of AI cameras and environmental sensors for intelligent analysis to identify abnormal behaviors such as abnormal daily activity patterns of the objects being cared for, providing strong support for health assessment. The millimeter-wave radar has strong anti-interference ability, can effectively resist interference from other electronic devices, and improve the accuracy of monitoring. The AI camera can capture the facial expression details of the object being cared for, such as frowning, smiling, blinking, etc. These subtle expression changes can often reflect a person's emotional state and health condition. The millimeter-wave radar can accurately sense the limb movements of the object being cared for and even detect minute movements such as vital signs like breathing and heartbeat, helping to detect abnormal conditions in a timely manner and providing accurate data support for health analysis. Combining the AI camera and the millimeter-wave radar can achieve comprehensive monitoring of the facial expressions and limb movements of the object being cared for. The AI camera is responsible for capturing facial expressions, while the millimeter-wave radar is responsible for sensing limb movements and vital signs. The two complement each other and jointly build a comprehensive health monitoring system, which can more accurately assess the health condition of the object being cared for and detect potential health problems in a timely manner. Using the AI camera and the millimeter-wave radar for real-time data collection enables any expression change or limb movement of the object being cared for to be immediately captured, and thus health problems can be detected and processed in a timely manner. Especially in emergency situations, it can quickly respond to ensure the safety of the object being cared for. When an abnormal health state of the cared-for object is detected, it will notify the family members or caregivers of the object being cared for in a timely manner through multiple alarm methods such as APP, text message, and phone call, ensuring that the family members or caregivers of the object being cared for can quickly receive the abnormal information and take corresponding countermeasures, effectively ensuring the safety and health of the cared-for object.

[0038] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0039] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. The intelligent health care monitoring system is characterized by: include: The monitoring module is used to use monitoring equipment to capture facial videos and scene images of the person being cared for, monitor the physical condition of the person being cared for in real time, and collect monitoring data in real time. The monitoring equipment includes AI cameras, millimeter-wave radars, and environmental sensors; An analysis module, used to analyze the environment in which the object being cared for is located and the facial expression characteristics of the object being cared for; and analyzing the respiratory and heart rate parameters of the person being cared for; The early warning module is used to judge the living environment, health status and behavior status of the object being cared for, send out an alarm signal according to the judgment result, and encrypt the data; Mobile terminal, used to achieve remote monitoring and management, adjust the monitoring angle and range of AI cameras, and provide voice intercom function.

2. The health care intelligent nursing monitoring system according to claim 1 is characterized by: The monitoring module comprises: An environmental monitoring module is used to capture facial videos and scene images of the object being cared for using monitoring equipment, and to process and extract features from the scene images; The health monitoring module is used to monitor the physical condition of the person being cared for in real time using millimeter-wave radar and collect monitoring data in real time; The data uploading module is used to upload the monitoring data of the environmental monitoring module and the monitoring data of the environmental monitoring module to the analysis module.

3. The intelligent health care monitoring system according to claim 2 is characterized in that: The health monitoring module specifically includes: Use millimeter-wave radar to emit electromagnetic waves to capture the signal reflected by the object in the path, and remove noise and clutter in the reflected signal, including removing DC components and normalizing amplitude; Fast Fourier transform is used for human body positioning, and the MTI algorithm is used to suppress static target echoes to obtain tiny motion signals caused by the breathing and heartbeat of the chest of the person being cared for; Perform an inverse tangent operation on the reflected signal, extract the phase information, and process the jump phenomenon of the phase information to obtain a continuous phase curve; Performing differential processing on the processed phase curve to extract the frequency range of the breathing and heartbeat signals in the phase curve; Extracting the envelope of the processed phase curve and performing normalization processing to obtain the final heartbeat waveform, and obtaining the heart rate of the object being cared for through the extracted heartbeat waveform; Smart bracelets are used to collect the blood pressure, blood oxygen and body temperature data of the person being cared for in real time to assist in managing and relieving emotions and to warn of mental health conditions.

4. The health care intelligent nursing monitoring system according to claim 2 is characterized by: The environmental monitoring module comprises: Configure the monitoring area of ​​the AI ​​camera and the monitoring parameters of the environmental sensor according to the care needs; Use AI cameras to continuously capture facial videos and scene images of the person being cared for at a specific frame rate, and capture subtle changes in facial expressions of the person being cared for in the facial videos; De-noising, gray-scaling and image enhancement processing are performed on the collected scene images, and feature extraction is performed on the processed images; Environmental sensors are used to monitor the temperature, humidity and air quality parameters of the living environment in real time. Environmental sensors include temperature and humidity sensors, smoke sensors, noise sensors and combustible gas detectors.

5. The intelligent health care monitoring system according to claim 4 is characterized in that: The feature extraction of the processed image specifically includes: Use computer vision technology to detect and track faces in videos and extract face images in each frame; Extract expression features from facial images and analyze the extracted expression features.

6. The intelligent health care monitoring system according to claim 1 is characterized in that: The analysis module comprises: A data receiving module, used to receive the environmental monitoring data and health monitoring data of the object of care collected by the monitoring module; An environmental analysis module is used to analyze the environment of the object under care and the facial expression characteristics of the object under care according to the monitored environmental monitoring data; The health analysis module is used to analyze the breathing and heart rate parameters of the cared-for object, and calculate the real-time average speed in the height and horizontal directions and the real-time horizontal distance change of the cared-for object.

7. The intelligent health care monitoring system according to claim 6 is characterized by: The environmental analysis module comprises: Analyze the air quality and noise level of the living environment of the person being cared for using the monitoring parameters of the environmental sensors; Image recognition technology is used to analyze the images captured by the AI ​​camera and identify the physical characteristics of the living environment of the person being cared for.

8. The health care intelligent nursing monitoring system according to claim 6 is characterized by: The health analysis module comprises: Analyze the skin expansion movement of the cared object caused by breathing and heartbeat by using the breathing frequency and heart rate of the cared object, and invert the breathing and heart rate parameters according to the skin expansion movement caused by breathing and heartbeat; Analyze the frequency, amplitude and phase parameters of the millimeter-wave radar reflection signal, and calculate the position, velocity and acceleration of the object being cared for based on the frequency, amplitude and phase parameters of the millimeter-wave radar reflection signal; The calculated data is subjected to mean filtering and piecewise linear fitting to obtain the approximate motion law of the object being cared for; The approximate motion law is analyzed to obtain the real-time average speed of the object being cared for in the height and horizontal directions and the real-time horizontal distance change.

9. The health care intelligent nursing monitoring system according to claim 1 is characterized by: The early warning module comprises: A judgment module is used to judge the living environment of the care recipient, the health status of the care recipient, and the situation of the care recipient falling or being lost; An alarm module is used to send the judgment result of the judgment module to the terminal of the family member or caregiver of the cared object, and send a corresponding alarm signal according to the judgment result; The security protection module is used to encrypt the received monitoring data and judgment results, identify the identity of the accessing user and allocate permissions, and identify the identity of the accessing user during the data access process.

10. The intelligent health care monitoring system according to claim 9 is characterized in that: The judging module comprises: Preset thresholds for the average speed of the object being cared for in the height and horizontal directions and the change in horizontal distance; Compare the real-time average speed of the object being cared for in height and horizontal direction and the real-time horizontal distance change with the preset threshold value to determine whether the object being cared for has fallen; Use air quality to determine whether the living environment has an impact on the respiratory system of the person being cared for, and whether there are safety hazards such as gas leaks, open flames, and prolonged smoking in the living environment of the person being cared for; Use noise levels to determine whether the living environment has an impact on the sleep quality of the person being cared for; Determine whether the subject's sleep duration, tossing and turning times, apnea times, and breathing frequency are abnormal based on the subject's breathing and heart rate parameters; The emotional state of the person being cared for is determined by using the facial expression features of the person being cared for, and the emotional state of the person being cared for is integrated and analyzed with the breathing and heart rate parameters of the person being cared for to determine the psychological state and health risks of the person being cared for.

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