53 results about "Elderly falls" patented technology

The invention discloses an old people tumble alarm system and method based on multiple sensors, and relates to the technical field of intelligent monitoring, and the system collects the body data of old people through an intelligent bracelet and a sensor group, transmits the body data to a cloud platform through a wireless network, carries out the processing, and generates a state data group. The acceleration modulus length, the angular velocity modulus length, the gait cycle and the posture angle of the old people are calculated, so that the motion state and the falling risk are accurately judged, a falling risk assessment coefficient is calculated based on the data, old people falling assessment is performed according to the falling risk assessment coefficient and a preset falling risk threshold value, and an alarm is triggered and a guardian is notified when the risk exceeds the standard. The heart rate variability index and the blood oxygen content are analyzed, a comprehensive health index is obtained through calculation, possible body injury after falling is evaluated, and injury alarm information is generated. The system can accurately monitor the health state of the old people in real time, timely send out early warning and alarm information, and provide comprehensive fall prevention and health monitoring guarantee.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

The invention relates to the technical field of indoor old people safety monitoring, and discloses an indoor old people tumble detection system based on a hybrid mode, which comprises a video acquisition module, an audio acquisition module, an identity recognition module, a tumble detection module, an emotion recognition module and an alarm module. According to the method of the indoor old people tumble detection system based on the mixed mode, video and audio data are creatively fused, multi-dimensional monitoring of the state of the old people, capturing of action postures of the old people by the video acquisition module and obtaining of voice and environment sound information by the audio acquisition module are achieved, and the two complements each other; abnormal sound in the audio, such as collision sound of falling and shouting sound, can assist in judgment, the multi-dimensional data acquisition and analysis mode greatly reduces misjudgment and missed judgment caused by single data, the accuracy and reliability of detection are remarkably improved, and the safety of old people is comprehensively guaranteed.

The invention discloses an intelligent monitoring method and system for preventing an old person from falling down based on multiple sensors, and relates to the technical field of falling monitoring, and the method comprises the following steps: merging behavior data, physiological data and environmental data into a data set; obtaining a feature combination from the related features based on an association rule, and quantifying the influence of the feature combination on the falling probability of the elderly; based on a machine learning algorithm, learning a relationship between the related features and whether the old person falls down, and predicting an old person falling probability corresponding to each sample of the new data set; optimizing the falling probability of the elderly based on the quantification result; if the optimized falling probability of the old people exceeds a certain threshold value, executing a corresponding early warning scheme; the system comprises a data acquisition module, a feature combination acquisition module, a probability prediction module, a probability optimization module and an early warning execution module. According to the invention, the fall probability corresponding to each sample can be obtained, the quantitative influence of the detected feature combination is superposed to the prediction result, and the accuracy of fall risk assessment is further improved.

The invention discloses an elderly falling early warning and intervention method based on emotion recognition and behavior pattern analysis. The elderly falling early warning and intervention method comprises the following steps: S1, collecting a facial expression data set, a voice signal data set and a behavior data set of an elderly; s2, analyzing the collected facial expression data set by using a convolutional neural network model; s3, analyzing the voice signal data set by using a long short-term memory network model; s4, an ElderSense model is set; and S5, when the analysis result shows that the risk score is higher than a preset first threshold value, sending an early warning signal to the old people through the intelligent equipment, and when the analysis result shows that the falling risk is higher than a preset second threshold value, sending an early warning signal to the old people and caregivers thereof through the intelligent equipment. The falling risk of the old people can be comprehensively evaluated from multiple angles, and the accuracy of falling risk judgment in a complex situation is improved.

The invention discloses a household elderly falling monitoring system and method based on feature fusion, and relates to the technical field of falling monitoring. The technical key points are as follows: firstly, the reasonability and effectiveness of an obtained detection result are guaranteed to obtain first gait image recognition of the home elderly to estimate the posture of the home elderly, the improved OpenPose network and CoordConv are utilized to replace VGG-16 into EfficientNet, the first gait of the home elderly is accurately monitored, the balance influence proportion is calculated for different gaits, and the balance influence proportion is calculated for the different gaits. According to the method, the probability that the old people have falling behaviors is judged to generate different risk signals, falling behaviors and postures under medium and high risk signals are further analyzed in a targeted mode, through deflection analysis, changes of human body postures are monitored in real time, characteristics of different falling postures are understood, different falling postures are independently and accurately modeled, and a second falling coefficient is generated; the tumble degree is accurately identified, and a foundation is laid for optimization of a subsequent model.

The invention discloses an old people fall monitoring and multi-terminal linkage emergency response system, and relates to the technical field of medical treatment, and the system comprises the steps: carrying out the non-inductive collection of human body dynamic information in a weak light visual blind area environment; the voice interaction verification judgment module comprises a far-field voice excitation unit; and setting a priority queue management mechanism, and confirming a behavior perception output tumble instruction. According to the invention, closed-loop management of fall monitoring and emergency treatment of the old people is realized, and the system has remarkable beneficial effects; the privacy of the user is effectively protected, and the false alarm rate and the invalid scheduling of nursing resources are obviously reduced; the event discrimination accuracy and the individual suitability are improved, and it is ensured that high-risk events are not missed; the timeliness and accuracy of emergency response are improved, and safe, intelligent and humanized technical support is provided for intelligent old-age care and home health monitoring.

The invention discloses an old people tumble detection system which is characterized in that human body acceleration data, human body inclination angle data and plantar pressure data are acquired and input into a judgment module, physical sign data are acquired and input into the judgment module, a tumble judgment result is acquired and input into an inflation module, and the inflation module is used for inflating the inflation module. According to the acceleration data, the human body inclination angle data, the plantar pressure data and the physical sign data, performing early warning judgment, obtaining early warning judgment, inputting an early warning judgment result into an inflation module, receiving a falling judgment result and the early warning judgment result, and performing inflation according to the received falling judgment result and the early warning judgment result. Accurate detection, personalized early warning, dynamic adjustment and intelligent inflation are integrated, through multi-sensor integration, motion data are analyzed in real time, tumble is accurately judged, inflation protection is carried out instantly, meanwhile, the tumble risk is evaluated according to physical sign data, multi-layer early warning is provided, the tumble risk of old people is effectively reduced, and safety guarantee is improved.

The invention discloses an intelligent early warning method and system for falling in a nursing home scene, and relates to the technical field of medical health, and the method comprises the steps: obtaining the static and dynamic data of an old person, carrying out the preprocessing and feature extraction of a data set, and forming a feature data set; classifying the feature data according to the data type, constructing a classification feature data set, and establishing an old man falling prediction model through a logic regression model, a linear combination of classification features and a Sigmoid function; and carrying out processing and feature extraction by using a pre-constructed old people tumble data set, and inputting the tumble data set into the prediction model for training to minimize a loss function value so as to obtain an optimized tumble prediction model. And finally, inputting the classification feature data into the optimized model, and outputting an old man falling prediction result.

The invention relates to the field of home tumble detection, aims to solve the problems that a home tumble detection system for old people is single in detection means, is easily interfered, affects the detection accuracy, and is difficult to carry out two-way communication at the first time after tumble, and particularly relates to a home tumble detection system based on two-way communication of the Internet of Things. According to the invention, the falling situation possibly existing in the home environment is collected through three modes of millimeter wave radar, sound detection and image collection, so that the accuracy of judging the falling situation is improved, the falling situation caused by missed judgment and misjudgment is avoided, the safety of the detected old people is ensured, and after the falling situation occurs, the safety of the old people is ensured. According to the system, the two-way communication function is automatically accessed through the system, so that air two-way communication is realized by means of the fixed sound acquisition assembly and the sound playing assembly, and when the elderly fall down, the elderly can be timely contacted with guardians or managers, so that the falling elderly can be timely rescued.

The invention discloses an old man falling alarm system based on a multi-modal artificial intelligence model. The old man falling alarm system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management system. The data acquisition module acquires video image data, wearable equipment data and environment data of the elderly; the multi-modal fusion module carries out fusion processing on data of different modals to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of falling of the old; when it is judged that the old fall down and the confidence reaches a preset threshold, the alarm module starts to give an alarm; and the background management system configures, manages and monitors the system. According to the invention, by fusing various modal data, efficient and accurate detection and timely alarm of the falling condition of the elderly are realized by using a deep learning technology, a powerful guarantee is provided for the safety and health of the elderly, and the method is suitable for the scenes of old-age care institutions, community old-age care service centers, families of the elderly living alone and the like.

The invention discloses an intelligent old-age care supervision system based on an AI intelligent agent, and the system comprises a wearable biological information collection intelligent agent which uses a GD32 chip as a core processing module and is used for collecting and receiving the basic information of a supervised person; the fixed environment variable acquisition agent uses an ESP8266 chip as a core processing module and is used for acquiring and receiving environment variable information; the fixed video acquisition and analysis intelligent agent is used for acquiring posture information of people in a public area and judging abnormal states of the people through AI identification; and the information processing center is used for summarizing information acquired by the three intelligent agents and sending the information to the friendly interaction intelligent agent for a user to call and check. The method is high in applicability, low in misjudgment rate of fall detection of the old people and accurate in position identification.

The invention discloses an old people abnormal behavior remote monitoring and real-time early warning system, which comprises an equipment end, a cloud end and a mobile end, and is characterized in that the cloud end is in data connection with the equipment end and the mobile end, and the cloud end comprises an equipment information input module, an abnormal behavior analysis module, a real-time early warning module and a data storage module; the data storage module is in data connection with the equipment information input module, the abnormal behavior analysis module and the real-time early warning module. The main shoe monitoring end and the slave shoe monitoring end are additionally arranged at the bottom of the insole, so that the problem of missing wearing is solved, and the accuracy of abnormal behavior recognition is improved through data acquisition of double equipment ends; and the real-time early warning module performs graded early warning based on an analysis result of the abnormal behavior analysis module, so that monitoring resources are reasonably allocated, a complete safety guarantee system from data acquisition and analysis to early warning response is formed, the monitoring problem of falling and missing of the old people is effectively solved, and a comprehensive and efficient technical guarantee is provided for the safety of the old people.

The present invention discloses a method for assessing the risk of elderly people falling based on multidimensional data fusion, comprising the following steps: constructing a data set required for the elderly people falling risk assessment of a DNN model; inputting gait features, posture features and autocorrelation coefficients into different gate control unit GRU models respectively and realizing multidimensional data feature fusion through attention calculation; using the DNN model to classify the features extracted and spliced ​​by the GRU model, and outputting the probability of the elderly people falling risk. The method of the present invention is less affected by the environment and has high precision, so it can improve the accuracy of risk assessment.

The present invention relates to a method and system for evaluating the risk of falls for the elderly, belonging to the field of image processing and application of machine learning models. The evaluation method includes: (1) receiving the monitoring video data obtained by a camera installed in the monitored place; (2) cropping a human body image containing at least human joints from each frame of the monitoring video data, and processing the human body image into a human body image to be recognized with a predetermined standard size; (3) based on a pre-trained human pose estimation network model, obtaining the joint coordinate data in the human body image to be recognized; (4) based on the joint coordinate data of consecutive multiple frames of images, evaluating the risk of the elderly falling. Evaluating the risk of falling based on the human pose obtained by the machine learning model effectively reduces the usage of detection sensors, and reduces the trouble brought by wearing and the impact on the actions of the elderly. It can be widely applied to fields such as elderly care, etc.

The utility model discloses an old man falling alarm device which comprises a waistband body, an alarm mechanism is arranged at the front end of the waistband body, the alarm mechanism comprises a fixing plate fixedly connected to the front end of the waistband body, and a guide frame is fixedly connected to the front end of the fixing plate. The lower side, close to the guide frame, of the front end of the fixing plate is slidably connected with an alarm device body. According to the falling alarm device for the old people, the waistband main body penetrates through the inner side of the buckle belt ring and drives the alarm device main body to move towards one side, so that the alarm device main body is more tightly clamped by means of the butt joint surface and the contact surface, and the purpose of conveniently wearing the alarm device main body on the waist of the old people is achieved; and the waistband main body is a necessity for the old people to wear clothes, so that the old people cannot forget to wear the alarm device main body, and when the old people walk, the movement range of the alarm device main body is small, and the alarm device main body cannot be triggered easily.

The invention relates to the field of old people behavior monitoring, and discloses machine vision old people abnormal behavior protection equipment which comprises a protection garment for joint protection, a safety helmet, a fourth joint sleeve, a shoulder pad, a fifth joint sleeve, a third joint sleeve, a ring sleeve, a first joint sleeve and a second joint sleeve. The safety helmet, the fourth joint sleeve, the fifth joint sleeve, the third joint sleeve, the ring sleeve, the first joint sleeve, the second joint sleeve, the first monitoring mechanism and the safety mechanism are in signal communication through the Bluetooth module, and the third joint sleeve, the ring sleeve, the fourth joint sleeve, the first joint sleeve, the second joint sleeve and the safety helmet are each provided with a camera. Motion data are measured through a plurality of sensors, then accurate data of behaviors of the elderly are obtained, when the elderly suffers from diseases, the outer expansion sleeve can be instantly triggered, important joint parts are protected, damage to the elderly is reduced, and the overall survival rate is increased.

The invention discloses an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home, the intelligent detection system is composed of a plurality of sets of detection devices installed on the ceiling and the wall surface of a living space of the elderly, each set of detection device works independently, and each detection device comprises a multi-modal data acquisition module. Collecting movement, sound and environment data of the elderly; the data preprocessing module is used for carrying out denoising, filtering and feature extraction on the data; the tumble detection module judges whether the elderly tumble based on a deep learning algorithm; the alarm module is used for giving an alarm to an emergency contact person when falling is detected; and the storage module is used for storing various data. The detection method comprises the steps of data acquisition, preprocessing, fall judgment, alarm processing and data storage. The invention belongs to the technical field of smart home and health monitoring, and particularly relates to an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home.

The invention relates to the technical field of data processing and tumble detection, and discloses a method for reproducing a tumble injured part of an old person. The method comprises the following steps of: wearing a Bluetooth sensor on key parts of a wearer, determining the distance between key points by adopting an RSSI (Received Signal Strength Indicator) distance measurement method in which every two key points are mutually reference points, determining a coordinate range of the key points by utilizing an optimization algorithm, wearing an inertial sensor on the waist of the wearer, judging the basic posture of the wearer, and determining the coordinates of the key points in combination with the coordinate range of the key points; and then drawing a key point graph before and after falling, and analyzing the injured part according to the key point graph. According to the invention, the coordinate positions of the key points of the body before and after falling can be reproduced, so that the falling injured part of the elderly can be speculated, favorable reference is provided for determining examination items, unnecessary examination is reduced, and the elderly can be treated and cured more timely.

The utility model discloses a walking assisting device for preventing old people from falling down, which relates to the technical field of old people nursing and comprises a U-shaped frame, one end of the U-shaped frame is movably connected with symmetrical front wheels, two ends of the U-shaped frame are vertically connected with n-shaped frames through arc connecting rods, and the lower ends of the two symmetrical n-shaped frames are respectively connected with a pushing assembly through bent rods. An axle penetrates through the lower end of the n-shaped frame, and a traction rod is fixed to the surface of the axle through bolts. According to the walking aid device, the U-shaped frame is kept in a braking state by adjusting the using state of the rear wheel, and at the moment, the front wheel and the two ends of the U-shaped frame are supported, so that the walking aid device can be used as a multi-point supporting walking stick, and an injured lower limb is prevented from walking under stress for a long time; the front wheels and the rear wheels realize synchronous rolling walking, so that a user with one injured foot can perform injured lower limb rehabilitation exercise, and people with one injured foot can walk alternately with one foot and two feet in the rehabilitation exercise process.

The present application relates to the technical field of intelligent monitoring, in particular to an old people fall monitoring and early warning method and system based on multi-source information fusion, the system comprising a visual perception module, a wearable sensing module, a central processing unit and an early warning module. The method is as follows: collecting human posture video stream, human waist three-axis acceleration and three-axis angular velocity, foot pressure, heart rate and blood oxygen physiological data in the monitoring area; respectively analyzing visual posture information, inertial sensing and foot pressure data, and extracting key motion features; comprehensively analyzing the features of the three types of information sources, especially using the MediaPipe framework to efficiently analyze human posture, combining with wearable sensor data, adopting a multi-modal feature fusion strategy based on attention mechanism, improving the accuracy of multi-source information fusion, so as to accurately judge the stability of the current posture of the old people and the fall risk level, and timely trigger the early warning, improving the accuracy and safety of the monitoring.

The application discloses an old people fall risk assessment and dynamic protection intervention system and method, relates to the technical field of computer vision, and comprises a hardware component module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module and an application scenario module.The hardware component module is used for collecting the motion posture, gait and environment data of old people in real time through a multi-modal sensor, and providing hardware support for risk assessment and protection.The application realizes the deep fusion of visual, inertial and environment data through an attention mechanism, combines a light-weight CNN-LSTM network to quickly extract space-time features, and then accurately calculates the dynamic relationship between the center of mass and the support surface based on a biomechanical model, thereby effectively improving the real-time performance of posture analysis and the accuracy of fall risk assessment, reducing the consumption of computing resources, greatly improving the prediction accuracy of the system for the fall risk of old people, reducing the false alarm rate, and providing a reliable basis for a graded protection strategy.

The invention discloses a nursing home old man falling identification method and device based on a multi-modal large model, and the method comprises the steps: firstly obtaining an old man target bounding box coordinate set, and extracting a target region based on the old man target bounding box coordinate set to obtain an old man behavior image set; and then matching a question text for each old person behavior image in the old person behavior image set, inputting the question text into a multi-modal large model, and performing fall detection on the old person behavior image set to obtain a fall analysis answer list. And then semantic analysis and classification operation are performed on the tumble analysis answer list to obtain a tumble sample index set, and tumble event positioning and visual marking are performed on the nursing home video monitoring image based on the tumble sample index set and the old man target bounding box coordinate set to obtain a nursing home old man tumble identification annotation graph. Compared with a traditional detection method, the method has higher recognition accuracy and lower missing report rate.

The invention discloses a YOLOv11 attitude trajectory smoothing and abnormity early warning algorithm based on dynamic window weighted average, belongs to the technical field of computer vision, relates to human body attitude trajectory processing and abnormal behavior early warning, and is suitable for scenes such as smart pension and physical training. According to the technical scheme, the method is characterized in that a multi-task YOLOv11 model is constructed; a CSPDarknet53 backbone network and an attitude dedicated feature enhancement layer (APEL) are fused, and a target detection frame and 17 joint key points are synchronously output; dynamic window weighted smoothing: calculating the window size according to the centroid velocity v, and carrying out weighted average on continuous frame key points; according to the hierarchical early warning decision tree, three-level abnormal early warning is achieved through the joint angle (vector dot product formula) and the centroid speed. The method is mainly used for early warning of falling down of old people, standard detection of movement of athletes and other scenes, 35FPS is achieved on Jetson Nano, and the accuracy rate of key points is 92.3%.

The invention discloses a smart home travel safety monitoring system, and relates to the technical field of home-based care for the aged, a road network is established, the number and time of pedestrians on the road network are collected by using a monitoring device on a road, and the movement data of the pedestrians on the road network are analyzed by using big data; obtaining a relationship between pedestrians and time on each road in the road network; the position information and destination information of the old people are obtained, the probability that pedestrians pass through each road on the road network when the old people move on the road network is predicted, and the old people are guided to go to the destination to guide the path of the old people going out and select a proper route in combination with the lighting conditions on the road. The old people are helped to walk safely in the external environment; pedestrians on the road are used for carrying out auxiliary monitoring on the condition of the old people, the pedestrians provide help for the old people when the old people fall down, and harm caused by falling down to the old people is reduced.

The present invention discloses an intelligent elderly care method based on WiFi, and the steps are as follows: collect CSI data of the monitoring area through WiFi devices; preprocess the CSI data to determine whether it belongs to a static environment or a dynamic environment; when it is determined to be a static environment, train the preprocessed CSI data through GA-SVM to obtain a trained target model after machine learning, and use the trained target model to identify "staring blankly, sleeping, no one" in the CSI data, and then calculate the heart rate and breathing conditions during sleep; when it is determined to be a dynamic environment, train the preprocessed CSI data through GA-SVM to obtain a trained target model after machine learning, and use the pre-trained target to identify "elderly falling", "indoor intrusion", and other situations in the preprocessed CSI data; the static data processor and the dynamic data processor store the results in the memory, and train and update the pre-trained target model.

This invention belongs to the technical field of geriatric clinical rehabilitation equipment, specifically relating to an adjustable geriatric clinical exercise device, including a base, an upper limb exercise component and a lower limb exercise component mounted on the upper surface of the base, and an anti-fall component mounted on the base. The anti-fall component includes a first fixed seat mounted on the upper surface of the base, on which a hydraulic push rod is fixedly mounted. A seat is detachably mounted on the top of the hydraulic push rod, and the rear end of the seat is detachably connected to the backrest. Armrests are also mounted on both sides, and the bottom of the armrests is rotatably connected to the bottom of the seat. The invention incorporates an anti-slip pad on the base to increase the device's stability. The combination of a guardrail, a flexible layer, and a pressure alarm sensor forms comprehensive fall protection. The flexible layer cushions impacts, and the pressure alarm sensor provides timely warnings, reducing the risk of falls and injuries to the elderly. The armrests are foldable, providing support for the elderly to stand up, further enhancing safety.

The utility model relates to the technical field of elderly care, in particular to an elderly care system based on voice control and image processing, which comprises a high-definition camera; the background terminal is in communication connection with the high-definition camera, the background terminal comprises a central processing unit, an analysis module and a first communication module, and the central processing unit is electrically connected with the analysis module and the first communication module; the monitoring unit is used for receiving information sent by the background terminal and realizing remote monitoring; each action of the old people at home can be captured through the high-definition camera, so that all-weather real-time monitoring of the activity state of the old people is realized, various human body tumble states collected in advance are recorded into the analysis module, and the image of the high-definition camera is compared with the human body state through the central processing unit, so that the human body tumble state can be monitored in real time. Signals are transmitted to the monitoring unit through the first communication module, alarm information is quickly sent to preset contact persons such as family members or nursing personnel, and danger caused after the old people fall down is avoided.