A cloud-based intelligent control robot system for monitoring elderly health in nursing homes

Abstract

The present invention relates to the field of medical health technology, and in particular to a cloud-based intelligent control-based health monitoring robot system for elderly people in nursing homes. The system includes a health monitoring management generation module, a health monitoring execution and collection module, a health activity anomaly analysis module, and a remote monitoring alarm module. The system can obtain historical physiological health data and historical daily activity data and generate a health monitoring management plan using a cloud-based control platform; the cloud-based control platform can send health monitoring task instructions to the corresponding health monitoring robot and execute navigation to the location of the elderly in the nursing home. At the same time, the robot performs medicine and meal delivery tasks, conducts body recognition inquiries, and collects physiological health signals. At the same time, it locates health signal anomalies and responds to early warnings, generates health anomaly early warning signals corresponding to the elderly in the nursing home, and promptly sends early warning information to the corresponding medical staff or family members of the nursing home. The present invention can improve the efficiency of health monitoring management in nursing homes.

Description

Technical Field

[0001] The present invention relates to the field of medical health technology, and in particular to a cloud-based intelligent control-based robot system for monitoring the health of elderly people in nursing homes. Background Art

[0002] With the aging population, the health monitoring and nighttime safety of the elderly in nursing homes have become urgent social issues that need to be addressed. Currently, with the advancement of science and technology, intelligent technologies are gradually being applied to the elderly care industry. Many nursing homes have begun to introduce health monitoring devices, such as smart bracelets and smart mattresses, to monitor the physical condition of the elderly. However, most of these devices work independently, which can lead to the following technical pain points:

[0003] 1. Insufficient nighttime monitoring staff: Nursing homes have limited nighttime staff, making it difficult to provide real-time monitoring for all elderly people;

[0004] 2. Scattered monitoring equipment: Existing monitoring equipment is mainly fixed cameras, which have blind spots and lack active inspection capabilities;

[0005] 3. Delayed emergency response: When an elderly person falls, has difficulty breathing, or experiences other emergencies, fixed equipment is difficult to detect and handle in a timely manner;

[0006] 4. Low level of intelligence: Existing equipment lacks intelligent analysis and early warning capabilities, and is unable to actively monitor the physiological status of the elderly and provide early warning of abnormalities;

[0007] 5. Serious information silos: There is a lack of data interoperability and coordination between various monitoring devices, making it difficult to form a complete health monitoring closed loop. Summary of the Invention

[0008] Based on this, it is necessary for the present invention to provide a robot system for monitoring the health of the elderly in nursing homes based on cloud-based intelligent control to solve at least one of the above technical problems.

[0009] To achieve the above objectives, a cloud-based intelligent control-based elderly care institution health monitoring robot system includes the following modules:

[0010] The health monitoring management generation module is used to obtain the historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home, and generate the corresponding health monitoring management plan for the elderly in the nursing home based on the historical physiological health data and historical daily activity data using the cloud control platform;

[0011] The health monitoring execution and collection module is used to send corresponding health monitoring task instructions to the corresponding health monitoring robot based on the health monitoring management plan corresponding to the elderly in the nursing home and using the cloud control platform. Based on the health monitoring task instruction response, the module controls the corresponding navigation and autonomous mobile system of the health monitoring robot to navigate to the location of the elderly in the nursing home. At the same time, the module acts on the corresponding environmental interaction system to execute the medicine and meal delivery tasks according to the health monitoring tasks predetermined in the health monitoring management plan, and uses the elderly health information collection system to perform body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home;

[0012] The health activity anomaly analysis module is used to locate health signal anomalies in the physiological health signal spectra of the elderly in nursing homes based on their physical health feelings, so as to obtain the health signal anomaly points of the elderly in nursing homes; the health monitoring robot monitors the activity tracking posture status of the elderly in nursing homes in real time, and performs activity posture anomaly analysis based on the activity tracking posture status of the elderly in nursing homes, so as to obtain the activity tracking posture anomaly points of the elderly in nursing homes;

[0013] The remote monitoring alarm module is used to generate health abnormality warning signals corresponding to the elderly in the nursing home based on abnormal health signal locations or activity tracking posture anomalies, and send warning information to the corresponding medical staff or family members of the nursing home in a timely manner based on the cloud control platform.

[0014] Furthermore, the health monitoring management generation module includes the following functions:

[0015] Obtain historical physiological health data of residents in nursing homes, including heart rate, respiratory rate, blood pressure, and body temperature;

[0016] Obtain historical daily activity data corresponding to the elderly in nursing homes, including daily activity volume, movement trajectory, and posture changes;

[0017] The historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home are transmitted to the corresponding cloud control platform, and the cloud control platform is used to perform preliminary preprocessing on the historical physiological health data and historical daily activity data, including denoising, filtering and standardization, to remove the corresponding noise and interference information in the data. At the same time, the preprocessed historical physiological health data and historical daily activity data are individually classified and labeled and stored to generate a historical health monitoring data file corresponding to the elderly in the nursing home;

[0018] Utilize the cloud control platform and combine the historical health monitoring data files corresponding to the elderly in the nursing home to respond to the corresponding health monitoring robot to generate a corresponding health monitoring management plan for the elderly in the nursing home.

[0019] Furthermore, the health monitoring and management plan corresponding to the elderly in the nursing home specifically includes corresponding health monitoring autonomous navigation, healthy medication dosage, healthy diet status and activity tracking monitoring and management tasks.

[0020] Furthermore, the health monitoring execution and collection module includes the following functions:

[0021] Based on the health monitoring management plan corresponding to the elderly in the nursing home, the cloud control platform sends the health monitoring task instructions corresponding to the health monitoring management plan to the corresponding health monitoring robot, and the local control unit corresponding to the health monitoring robot receives and responds to the corresponding health monitoring task instructions to perform the health monitoring tasks corresponding to the elderly in the nursing home;

[0022] Based on the health monitoring task instruction, the corresponding local control unit responds and controls the corresponding navigation and autonomous movement system of the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home;

[0023] Based on the health monitoring task instructions, the environmental interaction system corresponding to the health monitoring robot performs the medicine and meal delivery tasks corresponding to the elderly in the nursing home according to the health medication dosage and health diet status management tasks predetermined in the health monitoring management plan, so as to autonomously navigate to the location of the elderly in the nursing home according to the medicine and meal delivery tasks, and realize the door opening and closing operation corresponding to the location through the degree of freedom robotic arm and anti-slip gripper. At the same time, the degree of freedom robotic arm and anti-slip gripper are used to safely and accurately complete the delivery of health medicines and meal items corresponding to the elderly in the nursing home;

[0024] The elderly health information collection system corresponding to the health monitoring robot is used to conduct body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in nursing homes.

[0025] Furthermore, the health monitoring task instruction acts on the corresponding local control unit to respond and control the navigation and autonomous movement system corresponding to the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home, including:

[0026] Based on the health monitoring task instructions, the corresponding local control unit responds to control the navigation and autonomous movement system of the health monitoring robot to build a three-dimensional map of the monitoring environment corresponding to the nursing home, so as to accurately locate the specific location of the elderly in the nursing home to be monitored;

[0027] Based on the three-dimensional map of the monitoring environment and combined The algorithm predicts the distribution of static obstacles between the elderly’s location and the health monitoring robot in the nursing home, and plans the path of the health monitoring robot based on the static obstacle distribution to generate a running distribution trajectory between the elderly’s location and the health monitoring robot;

[0028] According to the running distribution trajectory between the elderly person's location and the health monitoring robot, the current corresponding movement position, movement speed, movement direction and the surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory are obtained. Based on the current corresponding movement position, movement speed, movement direction and the surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory and combined with the dynamic window method, the feasible movement window of the health monitoring robot at each moment is analyzed;

[0029] Based on the movement direction and the surrounding real-time environmental obstacle distribution, the target trajectory of the health monitoring robot is optimized for each feasible movement window at each moment, so as to determine the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the movement window according to the movement direction, and based on the corresponding turning angle deviation, the corresponding obstacle avoidance operation trajectory of the health monitoring robot within the feasible movement window at each moment is planned and optimized to generate the target optimized movement trajectory of the health monitoring robot within each operation window;

[0030] Based on the target optimized motion trajectory of the health monitoring robot in each operation window, the corresponding health monitoring robot is autonomously navigated to the location of the elderly in the nursing home.

[0031] Furthermore, determining the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the movement window according to the movement direction includes:

[0032] By establishing a local coordinate system with the current position of the health monitoring robot as the origin within the motion window, and determining the direction and position of the health monitoring robot within the motion window and the distribution positions of various surrounding real-time environmental obstacles according to the local coordinate system based on the motion direction;

[0033] Perform relative position angle analysis based on the corresponding direction position of the health monitoring robot and the distribution positions of various surrounding real-time environmental obstacles, so as to calculate the relative position angle between the health monitoring robot and various surrounding real-time environmental obstacles using a geometric algorithm;

[0034] The maximum acceleration and maximum turning rate corresponding to the health monitoring robot in the motion window are obtained, and the turning angle deviation of the relative position angle between the health monitoring robot and each surrounding real-time environmental obstacle is calculated based on the maximum acceleration and maximum turning rate to obtain the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution in the motion window.

[0035] Furthermore, the use of the elderly health information collection system corresponding to the health monitoring robot to perform body recognition inquiry and physiological health signal collection includes:

[0036] The health monitoring robot's corresponding elderly health information collection system is used to conduct physical recognition inquiries, so as to actively inquire about the elderly's physical feelings during the process of delivering medicine and meals. During this period, the robot monitors and identifies the elderly's cries for help and groans, so as to obtain the elderly's physical health feelings.

[0037] Physiological health status monitoring is performed using the physiological health sign monitoring in the elderly health information collection system corresponding to the health monitoring robot, so as to collect the heart rate, respiratory rate and body temperature physiological health signals corresponding to the elderly in the nursing home through the corresponding non-contact sensors in the health monitoring robot, and obtain the physiological health signal status corresponding to the elderly in the nursing home;

[0038] Signal spectrum conversion is performed according to the physiological health signal conditions corresponding to the elderly in the nursing home to obtain the physiological health signal spectrum corresponding to the elderly in the nursing home.

[0039] Furthermore, the health activity abnormality analysis module includes the following functions:

[0040] Based on the corresponding physical health feelings of the elderly in nursing homes, the health signal abnormality of the physiological health signal spectrum corresponding to the elderly in nursing homes is located to obtain the abnormal sites of the health signals corresponding to the elderly in nursing homes;

[0041] The health monitoring robot monitors the activity tracking posture of the elderly in nursing homes in real time, including their daily activity and sleeping posture;

[0042] Conduct temporal change analysis on the activity tracking posture conditions corresponding to the elderly in nursing homes to generate a temporal change sequence of activity postures corresponding to the elderly in nursing homes;

[0043] An activity posture anomaly analysis is performed based on the time change sequence of activity posture corresponding to the elderly in nursing homes. If it is a time change sequence corresponding to daily activity volume, the time point of activity fall sudden occurrence corresponding to the elderly in nursing homes is statistically analyzed based on the time change sequence corresponding to daily activity volume; if it is a time change sequence corresponding to sleep body movement posture, the corresponding sleep body movement frequency is determined based on the time change sequence corresponding to sleep body movement posture, and the sleep activity abnormality node is determined for the time change sequence corresponding to the sleep body movement posture based on the sleep body movement frequency to determine the corresponding sleep activity abnormality time node, and the activity fall sudden occurrence time point and sleep activity abnormality time node corresponding to the elderly in nursing homes are merged to obtain the activity tracking posture anomaly corresponding to the elderly in nursing homes.

[0044] Furthermore, the health signal abnormality location of the physiological health signal spectra corresponding to the elderly in the nursing home based on the physical health feelings of the elderly in the nursing home includes:

[0045] Determine the corresponding time period for calling for help and groaning based on the corresponding physical health feelings of the elderly in the nursing home;

[0046] Based on the corresponding calling for help and groaning time periods of the elderly in nursing homes, the health signal anomaly positioning of the physiological health signal spectrum corresponding to the elderly in nursing homes is carried out, so as to determine the signal abnormality time points of the physiological health signal spectrum within the time period according to the calling for help and groaning time periods, including the time points of rapid rise, rapid fall and irregular oscillation, so as to obtain the health signal abnormality sites corresponding to the elderly in nursing homes.

[0047] Furthermore, the remote monitoring alarm module includes the following functions:

[0048] Generate health abnormality feedback information or activity posture abnormality feedback information corresponding to the elderly in the nursing home based on the abnormal health signal sites or activity tracking posture abnormality feedback corresponding to the elderly in the nursing home, and upload the feedback to the cloud control platform;

[0049] The cloud control platform is used to respond to early warnings based on the abnormal health feedback information or abnormal activity posture feedback information corresponding to the elderly in the nursing home, generate abnormal health warning signals corresponding to the elderly in the nursing home, and send early warning information corresponding to the elderly in the nursing home to the corresponding medical staff or family members of the nursing home in a timely manner.

[0050] Beneficial effects of the present invention:

[0051] The cloud-based intelligent control-based elderly health monitoring robot system proposed in this invention generally consists of a health monitoring management generation module, a health monitoring execution and collection module, a health activity anomaly analysis module, and a remote monitoring and alarm module. Compared with the existing technology, the beneficial effect of this application is that by obtaining the historical physiological health data and daily activity data of the elderly in the nursing home, it is key to building a health monitoring management plan. By collecting and analyzing this data, a comprehensive understanding of the elderly's health status and living habits can be obtained, forming a personalized health management profile. The key to this process is that it can solve the problem of information silos in traditional nursing homes and break the fragmented situation of various types of health and activity data. Through centralized management of the cloud control platform, nursing homes can track and analyze the health status of each elderly person in real time, making management more accurate and comprehensive. This not only improves the intelligence level of nursing services, but also can customize personalized health management plans for each elderly person, including corresponding health monitoring autonomous navigation, healthy medication dosage, healthy diet status, and activity tracking monitoring and management tasks, to predict potential health problems in advance, further improving the resource utilization of nursing homes. Secondly, after receiving precise health monitoring task instructions through the cloud platform, the health monitoring robot can efficiently perform health management tasks. The key to this process is that it can solve the problems of insufficient manpower, scattered equipment, and delayed emergency response in traditional nursing homes. The health monitoring robot can not only automatically navigate to the location of the elderly, avoiding monitoring delays caused by manpower shortages, but also autonomously perform daily tasks such as delivering medicine and meals, ensuring that the elderly receive timely life care and nursing. This automated operation reduces the demand for manpower, allowing caregivers to focus on tasks that require more interpersonal care, thereby improving work efficiency. In addition, the integration of the elderly health information collection system enables the robot to obtain the elderly's physiological health signals in real time and generate physiological health signal spectra based on this data. By continuously monitoring the elderly's health status, the system can adjust care strategies in a timely manner and provide customized health management for the elderly. Then, by using the physiological health signal spectra to locate health signal anomalies, it can effectively identify health anomalies in the elderly. The key to this process is that it can detect potential problems in health signals in real time, such as abnormal heart rate and blood pressure fluctuations, to help medical staff intervene early. This precise health monitoring method can reduce the risk of sudden illness or health crises and improve the safety of the elderly. At the same time, the health monitoring robot can also monitor the activities of the elderly in real time, track and analyze the elderly's activity postures, and identify abnormal movements or behaviors. This activity tracking and posture abnormality analysis can help detect falls or abnormal sleep movements of the elderly during movement, and provide timely warnings and take countermeasures.Finally, the cloud-based control platform enables automatic early warning of health anomalies, significantly improving the emergency response capabilities of nursing homes. When health signals or activity postures show abnormalities, early warning signals can be quickly issued and the information can be passed on to relevant medical staff or family members. This early warning mechanism not only improves the efficiency of handling emergencies, but also effectively avoids the deterioration of health problems caused by delayed emergency response. Especially at night or when there is no one on duty, the intelligent system can monitor the health status of the elderly around the clock to ensure that any abnormal situation can be dealt with in a timely manner, greatly enhancing the elderly’s sense of security and their families’ sense of trust, and by reasonably arranging the work of nursing staff, ensuring that every elderly person can receive professional health care at the most appropriate time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0053] Figure 1 This is a module diagram of the cloud-based intelligent control-based elderly health monitoring robot system for nursing homes of the present invention;

[0054] Figure 2 for Figure 1 Functional flow diagram of the health monitoring management generation module;

[0055] Figure 3 for Figure 1 Functional flow diagram of the health monitoring execution acquisition module. DETAILED DESCRIPTION

[0056] The following is a clear and complete description of the technical system of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0057] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0058] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0059] To achieve this, please refer to Figures 1 to 3 The present invention provides a cloud-based intelligent control-based elderly care institution health monitoring robot system, which includes the following modules:

[0060] The health monitoring management generation module is used to obtain the historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home, and generate the corresponding health monitoring management plan for the elderly in the nursing home based on the historical physiological health data and historical daily activity data using the cloud control platform;

[0061] The health monitoring execution and collection module is used to send corresponding health monitoring task instructions to the corresponding health monitoring robot based on the health monitoring management plan corresponding to the elderly in the nursing home and using the cloud control platform. Based on the health monitoring task instruction response, the module controls the corresponding navigation and autonomous mobile system of the health monitoring robot to navigate to the location of the elderly in the nursing home. At the same time, the module acts on the corresponding environmental interaction system to execute the medicine and meal delivery tasks according to the health monitoring tasks predetermined in the health monitoring management plan, and uses the elderly health information collection system to perform body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home;

[0062] The health activity anomaly analysis module is used to locate health signal anomalies in the physiological health signal spectra of the elderly in nursing homes based on their physical health feelings, so as to obtain the health signal anomaly points of the elderly in nursing homes; the health monitoring robot monitors the activity tracking posture status of the elderly in nursing homes in real time, and performs activity posture anomaly analysis based on the activity tracking posture status of the elderly in nursing homes, so as to obtain the activity tracking posture anomaly points of the elderly in nursing homes;

[0063] The remote monitoring alarm module is used to generate health abnormality warning signals corresponding to the elderly in the nursing home based on abnormal health signal locations or activity tracking posture anomalies, and send warning information to the corresponding medical staff or family members of the nursing home in a timely manner based on the cloud control platform.

[0064] In the embodiment of the present invention, please refer to Figure 1 The figure shows a module diagram of a cloud-based intelligent control-based elderly care institution elderly health monitoring robot system of the present invention. In this example, the cloud-based intelligent control-based elderly care institution elderly health monitoring robot system includes the following modules:

[0065] S1: Health monitoring management generation module, used to obtain historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home, and generate a health monitoring management plan for the elderly in the nursing home based on the historical physiological health data and historical daily activity data using the cloud control platform;

[0066] In an embodiment of the present invention, various specialized devices are used in nursing homes to collect historical physiological health data and daily activity data for elderly residents. An electronic blood pressure monitor is used to measure the elderly's blood pressure three times a day, morning, noon, and evening. After measurement, the device automatically stores the systolic and diastolic blood pressure data in a local database as digital signals. A smart thermometer uses infrared sensing technology to automatically measure and upload the elderly's temperature every hour to the database. A smart wristband, equipped with built-in photoelectric sensors and accelerometers, collects heart rate and respiratory rate data five times per second and transmits it to a local storage device via Bluetooth. Furthermore, cameras are installed in various areas of the nursing home to capture the elderly's activities at a frame rate of 30 frames per second. Image recognition technology and specific algorithms are used to count the number of times the elderly walk, stand, sit, and other behaviors, thereby calculating their daily activity level. A positioning wristband, equipped with a GPS module and an inertial measurement unit, is worn by the elderly and records location information and posture change data 10 times per second. This data is then used to map movement trajectories. This collected data is transmitted to a cloud-based control platform via a wired or wireless network at a rate of 1 MB per second. The platform uses data analysis algorithms to combine the elderly's historical physiological health data and daily activity data to generate a health monitoring and management plan. For example, based on the elderly's blood pressure, heart rate data and daily activity level, we can determine the appropriate dosage of health medication and diet plan, and plan the patrol route of the health monitoring robot based on its daily activity trajectory.

[0067] S2: A health monitoring execution and collection module is used to send corresponding health monitoring task instructions to the corresponding health monitoring robot based on the health monitoring management plan corresponding to the elderly in the nursing home and using the cloud control platform. Based on the health monitoring task instruction response, the module controls the corresponding navigation and autonomous mobility system of the health monitoring robot to navigate to the location of the elderly in the nursing home. At the same time, the module acts on the corresponding environmental interaction system to execute the medicine and meal delivery tasks according to the health monitoring tasks predetermined in the health monitoring management plan, and uses the elderly health information collection system to perform body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home;

[0068] In an embodiment of the present invention, a cloud control platform generates a health monitoring management plan corresponding to the elderly in the nursing home and sends health monitoring task instructions to the corresponding health monitoring robot at a frequency of 10 times per second through a wireless communication network. After receiving the instruction, the local control unit of the health monitoring robot responds immediately. On the one hand, the navigation and autonomous movement system is activated. The laser radar carried by the robot scans the surrounding environment 15 times per second to obtain distance data, and the camera collects environmental images at 30 frames per second. The visual SLAM technology is combined with the laser radar data to build a real-time environmental map. Then, according to the pre-entered nursing home map and the elderly positioning bracelet signal, the path is planned by the triangulation positioning algorithm, and the robot autonomously navigates to the location of the elderly. On the one hand, the environmental interaction system is triggered. According to the medicine and meal delivery tasks predetermined in the health monitoring management plan, the robot arm and the anti-slip gripper are used. When arriving at the door of the elderly's room, the door lock position is identified by the visual sensor, and the robot arm is operated to open the door to accurately deliver health medicines and meal items. At the same time, the elderly health information collection system is started, the voice module sends a physical recognition query to the elderly, the language recognition module analyzes the elderly's answer, and non-contact physiological health signal collection equipment such as millimeter wave radar sensors collect heart rate and respiratory rate 5 times per second, and infrared body temperature sensors measure body temperature. The physiological health signals are processed by the Fourier transform algorithm, and finally the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home are obtained.

[0069] S3: Health activity anomaly analysis module, which is used to locate health signal anomalies in the physiological health signal spectra of the elderly in nursing homes based on their physical health feelings, so as to obtain the health signal anomaly points of the elderly in nursing homes; monitor the activity tracking posture status of the elderly in nursing homes in real time through the health monitoring robot, and perform activity posture anomaly analysis based on the activity tracking posture status of the elderly in nursing homes, so as to obtain the activity tracking posture anomaly points of the elderly in nursing homes;

[0070] In an embodiment of the present invention, by using the obtained physiological health signal spectrum corresponding to the elderly in the nursing home, with the help of special signal analysis software, the abnormal location of health signal is performed according to the elderly's physical health feelings. For example, if the elderly complain of dizziness, the analysis software takes the feedback time point as the center, intercepts 10 minutes of data before and after the physiological health signal spectrum, and compares it with the normal physiological signal fluctuation range. If it is found that the heart rate rises by more than 20 beats / minute within 1 minute, or the blood pressure drops by more than 20 mmHg within 2 minutes, it is determined to be a health signal abnormal site. The health monitoring robot uses the high-definition camera and infrared sensor on board to realize The elderly's activity tracking posture is monitored at all times. For daily activity, the number of movements per unit time is counted through image recognition. For sleep body movement posture, a low-light camera is installed in the sleeping area, and image analysis algorithms are used to identify movements such as turning over and kicking. According to these activity tracking posture conditions, a judgment model is set. For example, if the daily activity level suddenly drops to zero and does not recover within 3 minutes, it is judged as the sudden time point of activity fall combined with abnormal body posture. If the frequency of sleep body movement exceeds the normal range (such as 5-10 times per hour normally, more than 15 times) and the movements are disordered and violent, it is determined to be an abnormal sleep activity node, and the activity tracking posture anomalies are summarized.

[0071] S4: Remote monitoring and alarm module, which is used to generate health abnormality warning signals corresponding to the elderly in the nursing home according to the abnormal locations of health signals or abnormal activity tracking postures of the elderly in the nursing home and send warning information to the corresponding medical staff or family members of the nursing home in time based on the cloud control platform.

[0072] In an embodiment of the present invention, once an abnormal health signal site or activity tracking posture abnormality corresponding to an elderly person in a nursing home is detected, the cloud control platform immediately activates the early warning response mechanism. The platform pre-sets early warning rules for different abnormal situations. When abnormal information is received, an early warning message containing the identity of the elderly and details of the abnormal situation (such as an excessively high heart rate value, suspected time and place of a fall, etc.) is sent to the mobile phone of the medical staff of the nursing home through the SMS interface; at the same time, an early warning notification is pushed to the mobile phone APP of the elderly's family through the instant messaging interface, detailing the abnormal health condition of the elderly, and generating a health abnormality early warning signal corresponding to the elderly in the nursing home, ensuring that relevant personnel can know in time and take corresponding measures.

[0073] Furthermore, the health monitoring management generation module includes the following functions:

[0074] Obtain historical physiological health data of residents in nursing homes, including heart rate, respiratory rate, blood pressure, and body temperature;

[0075] Obtain historical daily activity data corresponding to the elderly in nursing homes, including daily activity volume, movement trajectory, and posture changes;

[0076] The historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home are transmitted to the corresponding cloud control platform, and the cloud control platform is used to perform preliminary preprocessing on the historical physiological health data and historical daily activity data, including denoising, filtering and standardization, to remove the corresponding noise and interference information in the data. At the same time, the preprocessed historical physiological health data and historical daily activity data are individually classified and labeled and stored to generate a historical health monitoring data file corresponding to the elderly in the nursing home;

[0077] Utilize the cloud control platform and combine the historical health monitoring data files corresponding to the elderly in the nursing home to respond to the corresponding health monitoring robot to generate a corresponding health monitoring management plan for the elderly in the nursing home.

[0078] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Schematic diagram of the functional flow of the health monitoring management generation module in this embodiment. The health monitoring management generation module includes the following functions:

[0079] S11: Obtain historical physiological health data corresponding to the elderly in nursing homes, including heart rate, respiratory rate, blood pressure and body temperature;

[0080] In an embodiment of the present invention, historical physiological health data corresponding to the elderly are collected through a variety of professional medical equipment in a nursing home, and an electronic blood pressure monitor is used to measure the elderly's blood pressure regularly. After each measurement, the device automatically stores the systolic and diastolic blood pressure data in the form of digital signals in a local database. A smart thermometer is used to measure the elderly's body temperature through infrared sensing technology, and the measurement results are automatically uploaded to the database every hour. For heart rate and respiratory rate, wearable devices such as smart bracelets are used, which have built-in photoelectric sensors and accelerometers to collect heart rate and respiratory rate data at a frequency of 5 times per second, and transmit the data to a local storage device via Bluetooth. For example, within a week, the physiological health data of an elderly person is continuously collected, and these data are summarized to form a historical physiological health data set including heart rate, respiratory rate, blood pressure and body temperature.

[0081] S12: Obtain historical daily activity data corresponding to the elderly in the nursing home, including daily activity volume, movement trajectory and posture changes;

[0082] In an embodiment of the present invention, historical daily activity data corresponding to elderly residents in nursing homes is obtained using a variety of monitoring devices. Cameras are installed in various areas of the nursing home, and image recognition technology is used to capture the elderly's activities at a frame rate of 30 frames per second. A specific algorithm is used to identify the elderly's movements, and the number of times they walk, stand, sit, and other behaviors is counted to calculate their daily activity. At the same time, the elderly wear a wristband with a positioning function, which has a built-in GPS module and inertial measurement unit, and records the elderly's location information and posture change data at a frequency of 10 times per second. Based on this location information, the elderly's movement trajectory can be mapped. For example, through a week of monitoring, the walking route, activity area, and posture changes of an elderly person in the nursing home are recorded every day, and the historical daily activity data, including daily activity amount, movement trajectory, and posture change information, can be compiled.

[0083] S13: Transmitting the historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home to the corresponding cloud control platform, and using the cloud control platform to perform preliminary preprocessing on the historical physiological health data and historical daily activity data, including denoising, filtering and standardization, to remove the corresponding noise and interference information in the data, and at the same time, perform individual classification and labeling on the preprocessed historical physiological health data and historical daily activity data for storage, so as to generate a historical health monitoring data file corresponding to the elderly in the nursing home;

[0084] In an embodiment of the present invention, the collected historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home are transmitted to the corresponding cloud control platform through a wired network or a wireless network at a transmission speed of 1MB per second. In the cloud control platform, special data processing software is used to perform preliminary preprocessing on the data. For the historical physiological health data and historical daily activity data, a median filtering algorithm is used to remove noise points in the data. For example, in blood pressure data, abnormally high or low data points caused by measurement errors are removed. The data is standardized using a normalization method, and physiological health data and daily activity data in different ranges are unified into the range of 0-1 to facilitate subsequent analysis. After the preprocessing is completed, the data is classified and labeled according to the personal information of the elderly, such as name, age, room number, etc., and stored in the cloud database, and finally a historical health monitoring data file corresponding to each elderly person in the nursing home is generated.

[0085] S14: Utilize the cloud control platform and combine the historical health monitoring data files corresponding to the elderly in the nursing home to respond to the corresponding health monitoring robot to generate a health monitoring management plan corresponding to the elderly in the nursing home.

[0086] In an embodiment of the present invention, a cloud-based control platform generates a health monitoring management plan for elderly people in nursing homes based on historical health monitoring data archives corresponding to the elderly in nursing homes using a data analysis algorithm. For autonomous navigation tasks, the optimal patrol path for the health monitoring robot is planned based on the elderly's daily activity trajectories and frequently occurring areas to ensure that the elderly's needs can be discovered in a timely manner. In terms of health medication dosage, the daily medication types and dosages are determined based on the elderly's historical physiological health data, such as blood pressure and heart rate data, as well as the doctor's diagnostic recommendations. For healthy eating conditions, a personalized diet plan is formulated based on the elderly's age, physical condition, and daily activity level, including food types and intake amounts. In activity tracking and monitoring management tasks, reasonable activity targets and abnormal posture warning thresholds are set based on the elderly's historical posture change data and activity level. These tasks are integrated to form a complete health monitoring management plan, and the plan is sent to the corresponding health monitoring robot in the form of instructions to guide it to perform the monitoring task.

[0087] Furthermore, the health monitoring and management plan corresponding to the elderly in the nursing home specifically includes corresponding health monitoring autonomous navigation, healthy medication dosage, healthy diet status and activity tracking monitoring and management tasks.

[0088] Furthermore, the health monitoring execution and collection module includes the following functions:

[0089] Based on the health monitoring management plan corresponding to the elderly in the nursing home, the cloud control platform sends the health monitoring task instructions corresponding to the health monitoring management plan to the corresponding health monitoring robot, and the local control unit corresponding to the health monitoring robot receives and responds to the corresponding health monitoring task instructions to perform the health monitoring tasks corresponding to the elderly in the nursing home;

[0090] Based on the health monitoring task instruction, the corresponding local control unit responds and controls the corresponding navigation and autonomous movement system of the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home;

[0091] Based on the health monitoring task instructions, the environmental interaction system corresponding to the health monitoring robot performs the medicine and meal delivery tasks corresponding to the elderly in the nursing home according to the health medication dosage and health diet status management tasks predetermined in the health monitoring management plan, so as to autonomously navigate to the location of the elderly in the nursing home according to the medicine and meal delivery tasks, and realize the door opening and closing operation corresponding to the location through the degree of freedom robotic arm and anti-slip gripper. At the same time, the degree of freedom robotic arm and anti-slip gripper are used to safely and accurately complete the delivery of health medicines and meal items corresponding to the elderly in the nursing home;

[0092] The elderly health information collection system corresponding to the health monitoring robot is used to conduct body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in nursing homes.

[0093] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 The functional flow diagram of the health monitoring execution and collection module in this embodiment includes the following functions:

[0094] S21: Based on the health monitoring management plan corresponding to the elderly in the nursing home, a health monitoring task instruction corresponding to the health monitoring management plan is sent to the corresponding health monitoring robot using the cloud control platform, and the local control unit corresponding to the health monitoring robot receives and responds to the corresponding health monitoring task instruction to perform the health monitoring task corresponding to the elderly in the nursing home;

[0095] In an embodiment of the present invention, a health monitoring management plan is formulated in advance for each elderly person by a nursing home. The plan clearly stipulates the specific content, time schedule and other information of the monitoring task. After the cloud control platform reads the plan, the corresponding health monitoring task instructions are sent to the corresponding health monitoring robot through the wireless communication network at a frequency of 10 times per second. The local control unit of the health monitoring robot is equipped with a high-performance signal receiver to receive these instructions in real time. Once the instruction is received, the local control unit immediately parses the instruction and identifies key information such as the task type and target object. For example, when receiving a task instruction to conduct daily health monitoring of a certain elderly person, the local control unit responds quickly, mobilizes the internal resources of the robot, and prepares to execute the corresponding health monitoring task for the elderly in the nursing home.

[0096] S22: Based on the health monitoring task instruction, the corresponding local control unit responds and controls the corresponding navigation and autonomous movement system of the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home;

[0097] In an embodiment of the present invention, based on the received health monitoring task instructions, the local control unit activates the navigation and autonomous movement system of the health monitoring robot. The laser radar carried by the robot scans the surrounding environment at a frequency of 15 times per second to obtain distance data. At the same time, the built-in camera collects environmental images at a speed of 30 frames per second. The visual SLAM technology is combined with the laser radar data to build and update the surrounding environment map in real time. The pre-recorded map data of the nursing home and the positioning bracelet signal worn by the elderly are used to accurately determine the location of the elderly through the triangulation positioning algorithm. Based on this information, the navigation system uses The algorithm plans the optimal path from the robot's current position to the elderly person's location. The robot autonomously navigates along the planned path by controlling the motor speed and steering. During the movement, it continuously adjusts its motion state based on sensor data to ensure that it reaches the elderly person's location accurately.

[0098] S23: Based on the health monitoring task instruction, the environmental interaction system corresponding to the health monitoring robot performs the medicine and meal delivery tasks corresponding to the elderly in the nursing home according to the health medication dosage and health diet status management tasks predetermined in the health monitoring management plan, so that the robot can autonomously navigate to the location of the elderly in the nursing home according to the medicine and meal delivery tasks, and realize the door opening and closing operation corresponding to the location through the degree of freedom robotic arm and the anti-slip gripper. At the same time, the degree of freedom robotic arm and the anti-slip gripper are used to safely and accurately complete the delivery of health medicines and meal items corresponding to the elderly in the nursing home;

[0099] In an embodiment of the present invention, the environmental interaction system of the health monitoring robot is triggered by a health monitoring task instruction to perform medicine delivery and meal delivery tasks. The task instruction contains information such as the predetermined amount of health medication and healthy diet status in the health monitoring management plan. The robot plans the path again according to the medicine delivery and meal delivery tasks and navigates to the location of the elderly in the nursing home. When arriving at the door of the elderly person's room, the robot uses the visual sensor installed at the front end of the robotic arm to identify the position of the door lock, and uses the free-degree-of-freedom robotic arm and anti-slip clamp to operate the door opening and closing. For example, the robotic arm extends to the door lock, and the anti-slip clamp accurately clamps the door handle. The motor drives the robotic arm to rotate a certain angle to open the door. After entering the room, the robot uses the robotic arm and anti-slip clamp to safely and accurately deliver health medicines and meal items to the elderly according to the placement requirements of the medicines and meal items in the task instruction, such as placing the medicine box containing the medicine steadily on the table in front of the elderly and gently placing the meal plate in the appropriate position.

[0100] S24: Use the elderly health information collection system corresponding to the health monitoring robot to perform body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home.

[0101] In an embodiment of the present invention, after the health monitoring robot completes the task of delivering medicine and meals, its elderly health information collection system is activated. The voice module in the system sends body recognition inquiries to the elderly in a gentle tone through the speaker, such as "How do you feel today? Are you feeling unwell?" At the same time, the language recognition module analyzes the elderly's answers in real time, and the robot's non-contact physiological health signal collection equipment, such as millimeter wave radar sensors and infrared body temperature sensors, starts working. The millimeter wave radar sensor collects the elderly's heart rate and respiratory rate information at a frequency of 5 times per second by transmitting and receiving millimeter waves; the infrared body temperature sensor accurately measures the body temperature by detecting the infrared radiation emitted by the elderly's body. The collected physiological health signals are sorted in time series, and the spectrum is converted using the Fourier transform algorithm to finally obtain the physiological health signal spectrum corresponding to the elderly in the nursing home, providing data support for subsequent health status analysis.

[0102] Furthermore, the health monitoring task instruction acts on the corresponding local control unit to respond and control the navigation and autonomous movement system corresponding to the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home, including:

[0103] Based on the health monitoring task instructions, the corresponding local control unit responds to control the navigation and autonomous movement system of the health monitoring robot to build a three-dimensional map of the monitoring environment corresponding to the nursing home, so as to accurately locate the specific location of the elderly in the nursing home to be monitored;

[0104] In this embodiment of the present invention, a health monitoring task instruction is issued to a local control unit, which then activates the health monitoring robot's navigation and autonomous mobility systems. The robot's onboard laser radar performs a full-scale scan of the nursing home environment at a rate of 20 times per second, acquiring a large amount of distance data point cloud information. Simultaneously, the robot's built-in camera captures images of the surrounding environment in real time. Using visual SLAM (Simultaneous Localization and Mapping) technology, combined with the laser radar data, the robot constructs a three-dimensional map of the nursing home's monitoring environment. During the map construction process, the map is compared and corrected with pre-recorded architectural drawings of the nursing home to ensure accuracy. Once the map is complete, the robot uses triangulation to precisely locate the specific location of the elderly person in the nursing home, using positioning base stations installed in various areas of the nursing home and a positioning bracelet worn by the elderly person. For example, the robot can be located in Room 3 on the east side of the second floor of the nursing home.

[0105] Preferably, based on the three-dimensional map of the monitoring environment and combined with The algorithm predicts the distribution of static obstacles between the elderly’s location and the health monitoring robot in the nursing home, and plans the path of the health monitoring robot based on the static obstacle distribution to generate a running distribution trajectory between the elderly’s location and the health monitoring robot;

[0106] In the embodiment of the present invention, based on the constructed three-dimensional map of the monitoring environment, the map data is input into the path planning algorithm module, which uses Algorithm for path planning, The algorithm takes the robot's current position as the starting point and the elderly person's position as the end point, while taking into account the static obstacle information in the map, such as walls, fixed furniture, etc. The algorithm continuously searches for the path with the minimum cost by calculating the cost function from each node to the starting point and the end point. During the search process, according to the distribution of static obstacles in the map, the algorithm excludes inaccessible areas. For example, if a wall is encountered, the algorithm will automatically avoid the area and re-search for a feasible path. After a series of searches and calculations, the algorithm generates a running distribution trajectory between the elderly person's position and the health monitoring robot. This trajectory bypasses all static obstacles and is a theoretically feasible path.

[0107] Preferably, the current corresponding movement position, movement speed, movement direction and surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory are obtained according to the running distribution trajectory between the elderly person's location and the health monitoring robot, and the feasible movement window of the health monitoring robot at each moment is analyzed based on the current corresponding movement position, movement speed, movement direction and surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory and combined with the dynamic window method;

[0108] In an embodiment of the present invention, the health monitoring robot moves along a previously generated running distribution trajectory, and its internal sensors collect real-time data on its current motion position, motion speed, motion direction, and surrounding real-time environmental obstacle distribution on the trajectory. The robot's odometer accurately records motion position and speed information, the gyroscope determines the motion direction, and the lidar continuously scans to obtain the surrounding real-time environmental obstacle distribution. These data are input into a dynamic window method analysis module, which generates multiple possible motion windows at each moment based on the robot's kinematic model and current motion state. The motion window refers to the area that the robot can reach within a certain time range. For example, at a certain moment, according to the robot's current speed and acceleration limits, three motion windows are generated, corresponding to different forward directions and speed changes. Each window takes into account the surrounding real-time environmental obstacle distribution to ensure that the robot will not collide with obstacles within the window.

[0109] Preferably, a target trajectory is optimized for a feasible motion window of the health monitoring robot at each moment based on the motion direction and the surrounding real-time environmental obstacle distribution, so as to determine a turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the motion window according to the motion direction, and a corresponding obstacle avoidance operation trajectory of the health monitoring robot within the feasible motion window at each moment is planned and optimized based on the corresponding turning angle deviation, so as to generate a target optimized motion trajectory of the health monitoring robot within each operation window;

[0110] In an embodiment of the present invention, a target trajectory is optimized for the health monitoring robot at each feasible motion window based on the robot's current motion direction and the distribution of obstacles in the surrounding real-time environment. Using the motion direction as a reference, a geometric algorithm, such as the law of cosines, is used to calculate the turning angle deviation between the health monitoring robot and each obstacle distribution in the surrounding real-time environment within the motion window. Assuming the robot's current motion direction is due east and an obstacle is detected to the left front within the motion window, the robot is calculated to need to turn left by a certain angle to avoid the obstacle. The difference between this angle and the ideal motion direction is the turning angle deviation. Based on this deviation, the obstacle avoidance trajectory of the health monitoring robot within each feasible motion window is planned and optimized. For example, the robot's speed and steering angle within the motion window are adjusted based on the turning angle deviation to generate a new obstacle avoidance trajectory. This trajectory enables the robot to avoid obstacles more safely and efficiently, ultimately generating a target optimized motion trajectory for the health monitoring robot within each motion window.

[0111] Preferably, the corresponding health monitoring robot is autonomously navigated to the location of the elderly in the nursing home based on the target optimized motion trajectory corresponding to the health monitoring robot in each operation window.

[0112] In an embodiment of the present invention, the health monitoring robot optimizes the motion trajectory according to the target corresponding to each operation window, and continuously adjusts its own motion parameters, such as speed and steering angle, to achieve autonomous navigation. The robot controls the speed and steering of the motor to move along the target optimized motion trajectory toward the location of the elderly in the nursing home. During the movement, it continuously monitors its own motion state and changes in the surrounding environment. If a new obstacle is found or the motion state changes, it returns to the previous step, generates the motion window again and optimizes it to ensure that the robot can accurately and safely reach the location of the elderly. For example, when approaching the room where the elderly is, the robot detects a temporarily placed stool at the door. By replanning the motion trajectory, it successfully avoids the stool and finally accurately reaches the location of the elderly, completing the autonomous navigation task.

[0113] Furthermore, determining the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the movement window according to the movement direction includes:

[0114] By establishing a local coordinate system with the current position of the health monitoring robot as the origin within the motion window, and determining the direction and position of the health monitoring robot within the motion window and the distribution positions of various surrounding real-time environmental obstacles according to the local coordinate system based on the motion direction;

[0115] In an embodiment of the present invention, a square motion window with a side length of 2 meters is established by taking the current position of the health monitoring robot as the origin during its movement. The movement direction of the robot is determined with the help of the gyroscope and accelerometer inside the robot, and a local coordinate system is constructed based on this. For example, if the robot moves toward the north, the north direction is set as the positive direction of the Y axis of the coordinate system, and the east direction is set as the positive direction of the X axis. The laser radar carried by the robot scans the surrounding environment at a frequency of 10 times per second to obtain distance information. Through the principle of triangulation, each reflection point scanned by the laser radar is positioned in the local coordinate system, thereby determining the distribution position of obstacles in the surrounding real-time environment. At the same time, the robot accurately records its own direction and position in the local coordinate system through its own odometer data.

[0116] Preferably, a relative position angle analysis is performed based on the corresponding direction position of the health monitoring robot and the distribution positions of each surrounding real-time environmental obstacle, so as to calculate the relative position angle between the health monitoring robot and each surrounding real-time environmental obstacle using a geometric algorithm;

[0117] In an embodiment of the present invention, the relative position angle is calculated using the law of cosines, a geometric algorithm, based on the previously determined orientation and position of the health monitoring robot and the distribution of obstacles in the surrounding real-time environment. Assuming the robot's position is point A and the position of an obstacle is point B, the length dAB of line segment AB is first calculated based on the coordinates of the two points (xA, yA) and (xB, yB) in the coordinate system. Then, based on the coordinate axis directions of the coordinate system determined by the robot's motion direction, vectors OA (the vector pointing from the robot's position to the origin) and OB (the vector pointing from the obstacle's position to the origin) are calculated. The relative position angle θ between the robot and the obstacle is calculated using the law of cosines formula: cosθ = (|OA|² + |OB|² - |AB|²) / (2×|OA|×|OB|). For example, if the robot's position coordinates are (1, 0) and the obstacle's position coordinates are (2, 2), the calculated relative position angle is approximately 45 degrees. This calculation process is repeated for all obstacles in the surrounding real-time environment, ultimately determining the relative position angle between the robot and each obstacle.

[0118] Preferably, the maximum acceleration and maximum turning rate corresponding to the health monitoring robot within the motion window are obtained, and the turning angle deviation of the relative position angle between the health monitoring robot and each surrounding real-time environmental obstacle is calculated based on the maximum acceleration and maximum turning rate to obtain the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the motion window.

[0119] In an embodiment of the present invention, the maximum acceleration and maximum turning rate data under different operating states are pre-stored in the health monitoring robot. For example, in normal walking mode, the maximum acceleration is set to 0.5m / s² and the maximum turning rate is 15 degrees per second. The actual acceleration and turning rate data of the robot in the current motion window are obtained and compared with the maximum acceleration and maximum turning rate. For each relative position angle, the turning angle deviation is calculated based on the robot's kinematic model, taking into account the influence of acceleration and turning rate on the motion trajectory. Assuming that the robot moves at maximum acceleration, during the turning process, according to the current relative position angle, the turning angle deviation is calculated. The ideal turning trajectory is predicted based on the position angle and maximum turning rate. The actual turning trajectory is compared with the ideal turning trajectory. By calculating the angle difference between the two, the turning angle deviation between the health monitoring robot and the surrounding real-time environmental obstacle distribution within the motion window is obtained. For example, within a certain motion window, the relative position angle between the robot and an obstacle is 30 degrees. Under the maximum acceleration and maximum turning rate, the angle difference between the ideal turning trajectory and the actual turning trajectory is 5 degrees, that is, the turning angle deviation corresponding to the obstacle is 5 degrees. Finally, the turning angle deviation between the health monitoring robot and the surrounding real-time environmental obstacle distribution within the motion window is obtained.

[0120] Furthermore, the use of the elderly health information collection system corresponding to the health monitoring robot to perform body recognition inquiry and physiological health signal collection includes:

[0121] The health monitoring robot's corresponding elderly health information collection system is used to conduct physical recognition inquiries, so as to actively inquire about the elderly's physical feelings during the process of delivering medicine and meals. During this period, the robot monitors and identifies the elderly's cries for help and groans, so as to obtain the elderly's physical health feelings.

[0122] In an embodiment of the present invention, when the health monitoring robot is performing the task of delivering medicine and meals, its built-in elderly health information collection system activates the body recognition inquiry function. The robot uses speech synthesis technology to actively ask the elderly about their physical feelings in a gentle and clear voice, such as "Hello, how do you feel today?" At the same time, the language recognition module in the system starts working. The module uses a deep learning algorithm to perform real-time analysis of the elderly's answers. In this process, the system can also use sound monitoring technology to monitor and identify the elderly's cries for help and groaning information. Once keywords such as "pain" and "uncomfortable" are captured, or abnormal groans are detected, the system immediately records the relevant information and integrates this information into the corresponding physical health feelings of the elderly in the nursing home. For example, if the elderly answer "I feel a little dizzy today", or if the elderly do not respond and make abnormal groans during the process, the system records the information and incorporates it into the physical health feeling data.

[0123] Preferably, the physiological health status is monitored by using the physiological health sign monitoring in the elderly health information collection system corresponding to the health monitoring robot, so as to collect the heart rate, respiratory rate and body temperature physiological health signals corresponding to the elderly in the nursing home through the corresponding non-contact sensors in the health monitoring robot, and obtain the physiological health signal status corresponding to the elderly in the nursing home;

[0124] In an embodiment of the present invention, the health monitoring robot is equipped with advanced non-contact sensors for collecting physiological health signs of the elderly. During daily monitoring, when the robot approaches the elderly, its internal infrared body temperature sensor starts working, and accurately measures the elderly's body temperature by detecting the infrared radiation emitted by the elderly's body. The measurement accuracy can reach ±0.1°C. At the same time, the millimeter wave radar sensor transmits millimeter waves and receives reflected waves. The heart rate and respiratory rate of the elderly are analyzed according to the changes in the reflected waves. The sensor collects these physiological health signals at a frequency of 5 times per second and transmits the data to the elderly health information collection system. The system preliminarily organizes and calibrates the collected data, removes outliers, and finally obtains the physiological health signal status corresponding to the elderly in the nursing home. For example, at a certain moment, the sensor collects the elderly's heart rate of 75 beats / minute, the respiratory rate of 18 times / minute, and the body temperature of 36.5°C. The system records these data as the physiological health signal status of the elderly at that moment.

[0125] Preferably, signal spectrum conversion is performed according to the physiological health signal conditions corresponding to the elderly in the nursing home to obtain a physiological health signal spectrum corresponding to the elderly in the nursing home.

[0126] In an embodiment of the present invention, a specialized signal processing algorithm is used to convert signal spectra based on previously acquired physiological health signal conditions corresponding to elderly residents in nursing homes. First, the collected physiological health signals, such as heart rate, respiratory rate, and body temperature, are arranged in chronological order to form time series data. Then, a fast Fourier transform (FFT) algorithm is used to convert the time domain signals into frequency domain signals. A corresponding spectrogram is generated by analyzing the signal strengths of different frequency components. Normally, a heart rate signal has a stable peak within a certain frequency range. If an abnormality occurs, the peak position or intensity will change. These spectrograms are integrated to form a spectrogram of the physiological health signals corresponding to the elderly residents in nursing homes. For example, the heart rate signal of an elderly person over a one-hour period is converted using an FFT algorithm to obtain a spectrum distribution within a specific frequency range. This spectrum distribution is a portion of the spectrum of the physiological health signals corresponding to the elderly person's heart rate during this time period.

[0127] Furthermore, the health activity abnormality analysis module includes the following functions:

[0128] Based on the corresponding physical health feelings of the elderly in nursing homes, the health signal abnormality of the physiological health signal spectrum corresponding to the elderly in nursing homes is located to obtain the abnormal sites of the health signals corresponding to the elderly in nursing homes;

[0129] In an embodiment of the present invention, when a suspected sound signal of crying for help and groaning is determined by the corresponding cry for help and groaning feeling information in the physical health feeling previously recognized by voice recognition, the sound analysis software is started. The software uses sound recognition technology to compare and analyze the collected sound according to the pre-recorded cry for help and groaning sound sample features. Once it is confirmed that it is the cry for help and groaning sound of the elderly, the software begins to record the start time of the sound. When the sound ends, the software records the end time, thereby determining the corresponding cry for help and groaning time period. At the same time, the corresponding non-contact sensors in the health monitoring robot continuously collect the elderly's physiological health signals, including heart rate, respiratory rate and body temperature, and generate physiological health data in the form of time series based on these signals. The robot's corresponding data analysis program retrieves the physiological health signal spectrum corresponding to the time period of calls for help and groans determined previously. The program analyzes the signal changes in the spectrum through the set algorithm. When the signal rises by more than 50% of the normal fluctuation range in a short period of time (such as within 1 minute), it is determined to be a rapid rise time point; if the signal drops by more than 50% of the normal fluctuation range in the same time, it is a rapid decline time point; when the signal fluctuation is chaotic and deviates from the normal fluctuation curve, and lasts for more than 2 minutes, it is determined to be an irregular oscillation time point. These time points are the abnormal health signal sites, and finally the abnormal health signal sites corresponding to the elderly in nursing homes are obtained.

[0130] Preferably, the health monitoring robot monitors the activity tracking posture of the elderly in the nursing home in real time, including the daily activity and sleeping posture of the elderly in the nursing home;

[0131] In an embodiment of the present invention, a health monitoring robot is deployed in various areas of a nursing home, and the high-definition camera and infrared sensor it carries are used to track and monitor the activity tracking posture of the elderly in real time. The high-definition camera captures the elderly's activity images at a frame rate of 30 frames per second, and the infrared sensor is used to detect the elderly's thermal radiation, thereby accurately sensing the elderly's position and movements. For daily activity, the robot uses image recognition technology to identify the elderly's walking, standing, sitting, falling and other movements, and counts the number of movements per unit time to calculate the daily activity. For sleeping body movement posture, a low-light camera installed in the elderly's sleeping area continuously captures the elderly's sleeping state at night. The robot uses image analysis algorithms to identify the elderly's body movements such as turning over and kicking, records the body movement posture, and finally obtains the corresponding activity tracking posture status of the elderly in the nursing home.

[0132] Preferably, a time series change analysis is performed on the activity tracking posture conditions corresponding to the elderly in the nursing home to generate a time series of activity posture changes corresponding to the elderly in the nursing home;

[0133] In an embodiment of the present invention, the activity tracking posture status data corresponding to the elderly in the nursing home collected by the health monitoring robot is transmitted to the corresponding data analysis system in the robot. For daily activity, the activity value of the elderly in each time period is counted at intervals of 1 hour to form a daily activity time change sequence. For example, from 8 to 9 in the morning, the elderly walk 500 steps, stand up 10 times, fall down 1 time, etc. These data are recorded in the 8-9 o'clock time period. For sleep body movement posture, the number of body movements of the elderly per hour is also counted at intervals of 1 hour to generate a sleep body movement posture time change sequence. For example, at 10-11 o'clock at night, the elderly turn over 8 times and kick his legs 2 times, which are recorded in this time period. In this way, the activity tracking posture data of different time periods are arranged in chronological order, and finally a complete activity posture time change sequence is generated.

[0134] Preferably, an activity posture anomaly analysis is performed based on the time change sequence of activity posture corresponding to the elderly in the nursing home. If it is a time change sequence corresponding to the daily activity amount, the activity fall sudden time point corresponding to the elderly in the nursing home is obtained based on the statistical analysis of the time change sequence corresponding to the daily activity amount; if it is a time change sequence corresponding to the sleep body movement posture, the corresponding sleep body movement frequency is determined based on the time change sequence corresponding to the sleep body movement posture, and the sleep activity abnormality node is determined for the time change sequence corresponding to the sleep body movement posture based on the sleep body movement frequency to determine the corresponding sleep activity abnormality time node, and the activity fall sudden time point and the sleep activity abnormality time node corresponding to the elderly in the nursing home are merged to obtain the activity tracking posture anomaly corresponding to the elderly in the nursing home.

[0135] In an embodiment of the present invention, a fall judgment model is set based on the time change sequence corresponding to the daily activity level. If at a certain moment, the activity level of the elderly suddenly drops from the normal level to zero, and there is no sign of recovery in the next 3 minutes, and combined with the abnormal body posture of the elderly captured by the camera (such as the body tilt angle exceeds 60 degrees), this moment is determined to be the time point of the sudden activity fall. For the time change sequence corresponding to the sleep body movement posture, the frequency of body movement in each hour is calculated. If the body movement frequency in a certain hour exceeds the normal range (such as the normal sleep body movement frequency is 5-10 times per hour, when it exceeds 15 times), and the body movement is disordered and violent, it is determined that there is an abnormal sleep activity node in that hour. Finally, the sudden activity fall time point and the abnormal sleep activity node are summarized to obtain the activity tracking posture anomaly corresponding to the elderly in the nursing home, providing key information for subsequent health monitoring.

[0136] Furthermore, the health signal abnormality location of the physiological health signal spectra corresponding to the elderly in the nursing home based on the physical health feelings of the elderly in the nursing home includes:

[0137] Determine the corresponding time period for calling for help and groaning based on the corresponding physical health feelings of the elderly in the nursing home;

[0138] In an embodiment of the present invention, corresponding sound collection devices in the health monitoring robot, such as high-sensitivity microphone arrays, can collect environmental sounds in all directions and eliminate environmental noise interference through noise reduction algorithms, focusing on capturing the sounds made by the elderly. When the microphone array receives a sound signal suspected of calling for help and groaning, the sound analysis software is activated. The software uses sound recognition technology to compare and analyze the collected sound based on the pre-recorded call for help and groaning sound sample characteristics. Once it is confirmed that it is the elderly's call for help and groaning sound, the software begins to record the start time of the sound. When the sound ends, the software records the end time, thereby determining the corresponding call for help and groaning time period. For example, in a certain room, the microphone array corresponding to the health monitoring robot detects the elderly's groaning at 10:15 am, and the software starts timing. At 10:20, the sound stops, and the software records this 5-minute time period as the call for help and groaning time period, and finally determines the corresponding call for help and groaning time period.

[0139] Preferably, health signal anomaly positioning is performed on the physiological health signal spectrum corresponding to the elderly in the nursing home based on the time periods of calls for help and groaning corresponding to the elderly in the nursing home, so as to determine the signal abnormality time points of the physiological health signal spectrum within the time period according to the time periods of calls for help and groaning, including the time points of rapid rise, rapid fall and irregular oscillation, so as to obtain the health signal abnormality sites corresponding to the elderly in the nursing home.

[0140] In an embodiment of the present invention, the corresponding non-contact sensors in the health monitoring robot continuously collect the physiological health signals of the elderly, including heart rate, respiratory rate and body temperature, and generate physiological health signal spectrograms in the form of time series. Based on the previously determined time period of calling for help and groaning, the corresponding data analysis program of the robot retrieves the physiological health signal spectrogram corresponding to the time period. The program analyzes the signal changes in the spectrogram through the set algorithm. When the signal rises by more than 50% of the normal fluctuation range in a short period of time (such as within 1 minute), it is determined to be a rapid rise time point; if the signal drops by more than 50% of the normal fluctuation range in the same time, it is determined to be a rapid rise time point. , it is the time point of rapid decline; when the signal fluctuation is chaotic and deviates from the normal fluctuation curve, and lasts for more than 2 minutes, it is determined to be an irregular oscillation time point. These time points are the abnormal sites of health signals. For example, during the time period of calling for help and groaning from 10:15 to 10:20, the heart rate signal spectrum rises by 20 times / minute within 1 minute at 10:17, drops by 15 times / minute within 1 minute at 10:18, and irregular oscillations occur from 10:19 to 10:20. Then the time points corresponding to 10:17, 10:18, and 10:19 to 10:20 are the abnormal sites of health signals.

[0141] Furthermore, the remote monitoring alarm module includes the following functions:

[0142] Generate health abnormality feedback information or activity posture abnormality feedback information corresponding to the elderly in the nursing home based on the abnormal health signal sites or activity tracking posture abnormality feedback corresponding to the elderly in the nursing home, and upload the feedback to the cloud control platform;

[0143] In an embodiment of the present invention, abnormal information is fed back by analyzing the abnormal health signal sites determined previously (i.e., the time nodes corresponding to the heart rate being higher than 120 beats / minute for 5 consecutive minutes) or the activity tracking posture abnormalities (i.e., the time nodes corresponding to the elderly rolling or groaning continuously (such as for more than 3 minutes) or falling posture (the body tilt angle is more than 60 degrees and lasts for more than 3 seconds)). After being summarized by the signal receiver in the health monitoring robot, it is uploaded to the cloud control platform through the network, and corresponding health abnormality feedback information (such as a description of the abnormal heart rate) or activity posture abnormality feedback information (such as details of a suspected fall) is generated.

[0144] Preferably, a cloud control platform is used to respond to early warnings based on abnormal health feedback information or abnormal activity posture feedback information corresponding to the elderly in the nursing home, generate abnormal health early warning signals corresponding to the elderly in the nursing home, and promptly send early warning information corresponding to the elderly in the nursing home to the corresponding medical staff or family members of the nursing home.

[0145] In an embodiment of the present invention, after receiving health abnormality feedback information or activity posture abnormality feedback information corresponding to the elderly in the nursing home through the cloud control platform, the early warning response mechanism is immediately activated. The platform pre-sets early warning rules for different abnormal situations. For example, when receiving health abnormality feedback information of abnormal heart rate, the platform judges the abnormality level according to the preset rules. If it is determined to be an emergency abnormality, the platform sends an early warning information containing the identity of the elderly and the abnormal situation (such as an excessively high heart rate value) to the mobile phone of the medical staff corresponding to the nursing home through the SMS interface; at the same time, the platform pushes an early warning notification to the mobile phone APP of the elderly’s family members through the instant messaging interface, detailing the elderly’s health abnormality, and generating a health abnormality early warning signal corresponding to the elderly in the nursing home, ensuring that relevant personnel can know the elderly’s abnormal condition in time and take corresponding measures.

[0146] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0147] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A cloud-based intelligent control robot system for elderly care institution health monitoring, characterized by: Includes the following modules: The health monitoring management generation module is used to obtain the historical physiological health data and historical daily activity data of the elderly in the nursing home, and generate the corresponding health monitoring management plan for the elderly in the nursing home based on the historical physiological health data and historical daily activity data using the cloud control platform; it includes the following functions: Obtain historical physiological health data of residents in nursing homes, including heart rate, respiratory rate, blood pressure, and body temperature; Obtain historical daily activity data corresponding to the elderly in nursing homes, including daily activity volume, movement trajectory, and posture changes; The historical physiological health data and historical daily activity data corresponding to the elderly in the nursing home are transmitted to the corresponding cloud control platform, and the cloud control platform is used to perform preliminary preprocessing on the historical physiological health data and historical daily activity data, including denoising, filtering and standardization, to remove the corresponding noise and interference information in the data. At the same time, the preprocessed historical physiological health data and historical daily activity data are individually classified and labeled and stored to generate a historical health monitoring data file corresponding to the elderly in the nursing home; Utilize the cloud control platform and combine the historical health monitoring data files of the elderly in the nursing home to respond to the corresponding health monitoring robot to generate a health monitoring management plan for the elderly in the nursing home; The health monitoring execution and collection module is used to send corresponding health monitoring task instructions to the corresponding health monitoring robot based on the health monitoring management plan corresponding to the elderly in the nursing home and using the cloud control platform. Based on the health monitoring task instruction response, the module controls the corresponding navigation and autonomous mobility system of the health monitoring robot to navigate to the location of the elderly in the nursing home. At the same time, the module acts on the corresponding environmental interaction system to execute the medicine and meal delivery tasks according to the health monitoring tasks predetermined in the health monitoring management plan, and uses the elderly health information collection system to perform body recognition inquiries and physiological health signal collection to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home. The module includes the following functions: Based on the health monitoring management plan corresponding to the elderly in the nursing home, the cloud control platform sends the health monitoring task instructions corresponding to the health monitoring management plan to the corresponding health monitoring robot, and the local control unit corresponding to the health monitoring robot receives and responds to the corresponding health monitoring task instructions to perform the health monitoring tasks corresponding to the elderly in the nursing home; Based on the health monitoring task instruction, the corresponding local control unit responds and controls the corresponding navigation and autonomous movement system of the health monitoring robot to perform autonomous navigation to the location of the elderly in the nursing home; including: Based on the health monitoring task instructions, the corresponding local control unit responds to control the navigation and autonomous movement system of the health monitoring robot to build a three-dimensional map of the monitoring environment corresponding to the nursing home, so as to accurately locate the specific location of the elderly in the nursing home to be monitored; Based on a three-dimensional map of the monitoring environment and combined with the A* algorithm, the static obstacle distribution between the elderly person's location in the nursing home and the health monitoring robot is predicted. The path of the health monitoring robot is planned based on the static obstacle distribution to generate a running distribution trajectory between the elderly person's location and the health monitoring robot. According to the running distribution trajectory between the elderly person's location and the health monitoring robot, the current corresponding movement position, movement speed, movement direction and the surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory are obtained. Based on the current corresponding movement position, movement speed, movement direction and the surrounding real-time environmental obstacle distribution of the health monitoring robot on the trajectory and combined with the dynamic window method, the feasible movement window of the health monitoring robot at each moment is analyzed; Based on the movement direction and the surrounding real-time environmental obstacle distribution, the target trajectory of the health monitoring robot is optimized for each feasible movement window at each moment, so as to determine the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the movement window according to the movement direction, and based on the corresponding turning angle deviation, the corresponding obstacle avoidance operation trajectory of the health monitoring robot within the feasible movement window at each moment is planned and optimized to generate the target optimized movement trajectory of the health monitoring robot within each operation window; Based on the target optimization motion trajectory of the health monitoring robot in each operation window, the corresponding health monitoring robot is autonomously navigated to the location of the elderly in the nursing home; Based on the health monitoring task instructions, the environmental interaction system corresponding to the health monitoring robot performs the medicine and meal delivery tasks corresponding to the elderly in the nursing home according to the health medication dosage and health diet status management tasks predetermined in the health monitoring management plan, so as to autonomously navigate to the location of the elderly in the nursing home according to the medicine and meal delivery tasks, and realize the door opening and closing operation corresponding to the location through the degree of freedom robotic arm and anti-slip gripper. At the same time, the degree of freedom robotic arm and anti-slip gripper are used to safely and accurately complete the delivery of health medicines and meal items corresponding to the elderly in the nursing home; The health information collection system for the elderly corresponding to the health monitoring robot is used to conduct body recognition inquiries and collect physiological health signals to obtain the corresponding physical health feelings and physiological health signal spectra of the elderly in the nursing home; The health activity anomaly analysis module is used to locate health signal anomalies in the physiological health signal spectra of the elderly in nursing homes based on their physical health feelings, so as to obtain the health signal anomaly points of the elderly in nursing homes; the health monitoring robot monitors the activity tracking posture status of the elderly in nursing homes in real time, and performs activity posture anomaly analysis based on the activity tracking posture status of the elderly in nursing homes, so as to obtain the activity tracking posture anomaly points of the elderly in nursing homes; The remote monitoring alarm module is used to generate health abnormality warning signals corresponding to the elderly in the nursing home based on abnormal health signal locations or activity tracking posture abnormalities, and send warning information to the corresponding medical staff or family members of the nursing home in a timely manner based on the cloud control platform.

2. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 1 is characterized in that: The health monitoring and management plan corresponding to the elderly in the nursing home specifically includes corresponding health monitoring autonomous navigation, healthy medication dosage, healthy diet status and activity tracking monitoring and management tasks.

3. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 1 is characterized in that: Determining the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution within the movement window according to the movement direction includes: By establishing a local coordinate system with the current position of the health monitoring robot as the origin within the motion window, and determining the direction and position of the health monitoring robot within the motion window and the distribution positions of various surrounding real-time environmental obstacles according to the local coordinate system based on the motion direction; Perform relative position angle analysis based on the corresponding direction position of the health monitoring robot and the distribution positions of various surrounding real-time environmental obstacles, so as to calculate the relative position angle between the health monitoring robot and various surrounding real-time environmental obstacles using a geometric algorithm; The maximum acceleration and maximum turning rate corresponding to the health monitoring robot in the motion window are obtained, and the turning angle deviation of the relative position angle between the health monitoring robot and each surrounding real-time environmental obstacle is calculated based on the maximum acceleration and maximum turning rate to obtain the turning angle deviation between the health monitoring robot and each surrounding real-time environmental obstacle distribution in the motion window.

4. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 1 is characterized in that: The method of using the elderly health information collection system corresponding to the health monitoring robot to perform body recognition inquiry and physiological health signal collection includes: The health monitoring robot's corresponding elderly health information collection system is used to conduct physical recognition inquiries, so as to actively inquire about the elderly's physical feelings during the process of delivering medicine and meals. During this period, the robot monitors and identifies the elderly's cries for help and groans, so as to obtain the elderly's physical health feelings. Physiological health status monitoring is performed using the physiological health sign monitoring in the elderly health information collection system corresponding to the health monitoring robot, so as to collect the heart rate, respiratory rate and body temperature physiological health signals corresponding to the elderly in the nursing home through the corresponding non-contact sensors in the health monitoring robot, and obtain the physiological health signal status corresponding to the elderly in the nursing home; Signal spectrum conversion is performed according to the physiological health signal conditions corresponding to the elderly in the nursing home to obtain the physiological health signal spectrum corresponding to the elderly in the nursing home.

5. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 4 is characterized in that: The health activity abnormality analysis module includes the following functions: Based on the corresponding physical health feelings of the elderly in nursing homes, the health signal abnormality of the physiological health signal spectrum corresponding to the elderly in nursing homes is located to obtain the abnormal sites of the health signals corresponding to the elderly in nursing homes; The health monitoring robot monitors the activity tracking posture of the elderly in nursing homes in real time, including their daily activity and sleeping posture; Conduct temporal change analysis on the activity tracking posture conditions corresponding to the elderly in nursing homes to generate a temporal change sequence of activity postures corresponding to the elderly in nursing homes; An activity posture anomaly analysis is performed based on the time change sequence of activity posture corresponding to the elderly in nursing homes. If it is a time change sequence corresponding to daily activity volume, the time point of activity fall sudden occurrence corresponding to the elderly in nursing homes is statistically analyzed based on the time change sequence corresponding to daily activity volume; if it is a time change sequence corresponding to sleep body movement posture, the corresponding sleep body movement frequency is determined based on the time change sequence corresponding to sleep body movement posture, and the sleep activity abnormality node is determined for the time change sequence corresponding to the sleep body movement posture based on the sleep body movement frequency to determine the corresponding sleep activity abnormality time node, and the activity fall sudden occurrence time point and sleep activity abnormality time node corresponding to the elderly in nursing homes are merged to obtain the activity tracking posture anomaly corresponding to the elderly in nursing homes.

6. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 5 is characterized in that: The method of locating health signal anomalies in the physiological health signal spectra corresponding to the elderly in the nursing home based on the physical health feelings of the elderly in the nursing home includes: Determine the corresponding time period for calling for help and groaning based on the corresponding physical health feelings of the elderly in the nursing home; Based on the corresponding calling for help and groaning time periods of the elderly in nursing homes, the health signal anomaly positioning of the physiological health signal spectrum corresponding to the elderly in nursing homes is carried out, so as to determine the signal abnormality time points of the physiological health signal spectrum within the time period according to the calling for help and groaning time periods, including the time points of rapid rise, rapid fall and irregular oscillation, so as to obtain the health signal abnormality sites corresponding to the elderly in nursing homes.

7. The cloud-based intelligent control-based elderly care institution health monitoring robot system according to claim 1 is characterized in that: The remote monitoring alarm module includes the following functions: Generate health abnormality feedback information or activity posture abnormality feedback information corresponding to the elderly in the nursing home based on the abnormal health signal sites or activity tracking posture abnormality feedback corresponding to the elderly in the nursing home, and upload the feedback to the cloud control platform; The cloud control platform is used to respond to early warnings based on the abnormal health feedback information or abnormal activity posture feedback information corresponding to the elderly in the nursing home, generate abnormal health warning signals corresponding to the elderly in the nursing home, and send early warning information corresponding to the elderly in the nursing home to the corresponding medical staff or family members of the nursing home in a timely manner.

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