2054 results about "Elderly people" patented technology

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

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 provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The invention is suitable for the technical field of fall detection and help calling, and provides a fall detection and help calling method for the elderly at home, and the method comprises the steps: collecting the multi-source health monitoring data of the elderly through a monitoring camera and a sensor, and carrying out the preprocessing based on the collected data; respectively extracting walking posture features, getting-up and sitting-down posture features, daily activity posture features, motion features, acceleration features, gyroscope features and heart rate features of the elderly; inputting the extracted features into a trained abnormal action detection model for action detection; when the old people have abnormal behavior actions, performing multi-dimensional analysis on the abnormal behavior actions of the old people by utilizing a clustering algorithm; and feeding back the analysis result of the abnormal behavior action of the old person in real time according to a preset help calling rule. According to the invention, the aims of comprehensively monitoring the health state of the elderly, timely discovering abnormity, deeply analyzing reasons and quickly responding to rescue can be fulfilled.

The invention discloses an intelligent control system and method based on an AI intelligent home model, and the system comprises a data acquisition module which is used for collecting home related data in real time based on a multi-dimensional sensing system, and the data comprises indoor and outdoor environment parameters and habitant physiological feature data; the data analysis module is used for performing resident comfort demand prediction analysis on the home related data based on the AI smart home model to obtain the comfort demand of the resident; and the environment regulation and control module is used for carrying out working parameter setting and working mode adjustment on the household electrical equipment based on the comfort degree requirement, so as to realize personalized environment adjustment for different residents and scenes. The AI smart home model accurately predicts the comfort demands of the residents, and the system can provide customized environment adjustment schemes for individual differences of different residents, meets the differentiated demands of special groups such as old people, children, pregnant women and the like, and improves the living experience.

The invention discloses an old people cognitive ability assessment system and method based on multi-modal fusion, and relates to the field of old people cognitive assessment, and the method comprises the steps: firstly obtaining the current multi-modal data of a user, and fusing the current multi-modal data into a current feature vector; then, instead of being compared with the universality standard, the personal baseline portrait of the user is called, and the score of the difference degree between the current state and the historical baseline of the user is calculated; particularly, the core index of the drift speed is introduced, and the difference degree and the historical change rate are combined, so that quantitative modeling is carried out on the changed speed. By analyzing the amplitude and the speed of the change at the same time, normal aging with gentle change and low speed and pathological recession with violent change and high speed can be effectively distinguished, so that the key technical problem that the normal aging and the pathological recession are easy to be confused in the background technology is accurately solved.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention discloses a whole-course accompanying robot for disabled old people and an automatic control system of the whole-course accompanying robot, relates to the technical field of intelligent accompanying, and is used for solving the problems of inaccurate positioning, untimely task response and poor service continuity in the accompanying process of the disabled old people. The multi-modal perception and state analysis module divides the accompanying environment into a plurality of sub-regions based on region characteristics, and accurately perceives the position and state change of the disabled elderly. And the task generation and scheduling execution module intelligently judges service requirements of disabled old people, and dynamically screens optimal robot resources for task matching and execution. The task feedback closed-loop module collects and analyzes task execution process and result data, and timely rechecks the state of the old person according to feedback. And the self-control module adjusts the accompanying strategy and periodically reviews the priority of the task queue. Through cooperative operation of the above modules, the system can achieve precise perception, intelligent decision and dynamic optimization of accompanying of disabled old people, and remarkably improves continuity, intelligence and reliability of accompanying service.

The invention discloses a community elderly weakness screening and intervention training method based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining dynamic physiological parameters and medical data, and generating a weakness evaluation parameter set; determining a target weakness level and generating a personalized training scheme in combination with the user tolerance parameter set; generating a user action quality evaluation result based on real-time action monitoring, and adjusting a training intensity parameter according to a baseline error threshold; and returning an evaluation result, updating the weakness evaluation parameter set, and generating a community-level weakness early warning signal according to a group risk threshold. The time sequence physiological data fusion and decision tree-transfer learning hybrid model of the wearable device is adopted, real-time optimization of a personalized training scheme and dynamic early warning of group weakness risks can be achieved, and screening accuracy and intervention effectiveness are remarkably 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 old man gait abnormity monitoring system based on body surface electromyographic signals, and relates to the technical field of gait abnormity monitoring, and the system comprises a configuration module which is used for obtaining a foot bearing device set of a user, and constructing three groups of foot bearing device test scenes; the inertial measurement module is used for collecting gait data of a user and establishing gait key parameters; the myoelectricity data acquisition module is used for establishing a myoelectricity test data set; the labeling module is used for establishing a gait cycle window and establishing a myoelectricity-gait fusion fingerprint spectrum; and the early warning module is used for reading the real-time acquired data and outputting an early warning signal. The technical problems that in the prior art, monitoring of the gait abnormity of the old man is not accurate enough, abnormity is difficult to find in time and early warning is difficult, and consequently the old man faces a high falling risk and cannot be protected in time when walking are solved, the accuracy and timeliness of monitoring of the gait abnormity of the old man are improved, and the safety of the old man is improved. Therefore, the walking safety of the old people is guaranteed.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

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 relates to the technical field of intelligent medical monitoring, in particular to a personalized health monitoring and early warning system for elderly people living alone fused with multi-dimensional data, and the system comprises a data collection and preprocessing module which collects multi-dimensional physiological parameter signals of a user and converts the multi-dimensional physiological parameter signals into multi-dimensional medical features; the health prediction module performs health category prediction on the multi-dimensional medical features of the current time node based on a pre-trained deep learning model, generates a data graph under each time node, and stores the data graph and a corresponding health category prediction result in a specified directory; the early warning module analyzes whether the multi-dimensional physiological parameter signals under each time node are abnormal or not in a mode of combining a hard threshold value and a soft threshold value, and if yes, an alarm is given out; and the abnormity output module calls and outputs the data maps of the preset duration before and after the abnormity alarm time node and the corresponding health categories. According to the invention, personalized monitoring and risk early warning of the health state of the elderly can be realized more accurately from multiple dimensions.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

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 waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

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 provides a radar-based off-bed detection method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining a multi-dimensional echo signal containing the information of a distance dimension, a speed dimension and an angle dimension; performing target detection based on the multi-dimensional echo signal, and generating a target 2D point cloud based on a target detection result; clustering processing is carried out based on the target 2D point cloud, feature extraction is carried out based on a clustering result, and a feature vector is generated; and inputting the feature vector into a pre-trained human body in-bed state judgment model to carry out off-bed event detection. According to the off-bed detection method provided by the invention, actual image data is not needed, real-time point cloud of nursing personnel on the bed can be provided under the condition of not invading privacy, the off-bed event can be reported in time, rapid detection of the off-bed event of long-term bedridden old people can be realized, the off-bed detection response time can be effectively shortened, and the detection efficiency is improved. And the accuracy and the real-time performance of off-bed event detection are effectively improved.

The invention discloses a method and system for guiding an old person to go home through a door lock, and the method comprises the steps: calculating a lost score value through a lost score model according to a condition that a target door lock A detects that the old person fails in continuous verification for multiple times and stays overtime in front of the door, and judging that the old person is in a lost state when the score value exceeds a threshold value, and obtaining a lost judgment result; according to the door lock A, encrypting and broadcasting the elderly biological characteristics to all the door locks, receiving confirmation information returned by the successfully matched door lock B, and obtaining a target room positioning result; according to the floor topological data of the door lock A and the door lock B, generating an optimal guide path, and obtaining path data containing a passing door lock sequence; according to the path data, controlling lamp strips along the way to be lightened according to a preset mode, and cooperating with a directional sound box to play voice guidance; and sending a reset instruction to the path door lock according to the fact that the door lock B detects that the old person successfully unlocks or arrives. According to the embodiment of the invention, convenient, efficient and intelligent door lock guiding can be carried out, and precise guiding and supporting for the elderly are realized.

The invention discloses a smart old-age service platform based on Internet of Things big data, and belongs to the technical field of Internet of Things old-age care, and the platform specifically comprises a voice instruction collection module which is used for collecting the interaction voice data of an old person and smart home equipment in real time; the voice feature extraction module is used for carrying out feature separation processing on the collected original voice data and extracting quantized instruction indexes; the baseline model construction module is used for establishing a voice health baseline model based on user characteristics and generating a personalized voice index reference interval according to the historical voice data of the old; the voice instruction comparison module is used for dynamically comparing the real-time instruction index with the baseline model, generating a voice health deviation degree curve, and triggering early warning when curve fluctuation exceeds a preset morphological characteristic; the interaction mode adjusting module is used for adjusting the interaction mode of the smart home equipment according to the voice health deviation degree; the health of the elderly is judged through the voice index change, and the smart home service quality is improved.

The invention provides an injection pen special for preventing acupuncture injury of insulin, and relates to the technical field of medical instruments, the injection pen comprises an injection pen body, the outer circumference of the injection pen body is provided with a metering observation window, the rear end face of the injection pen body is provided with an injection push key, and the front end face of the injection pen body is provided with a piston rod. The injection needle is sleeved with the inner needle cap and the outer needle cap, multiple protections are formed, it is ensured that the injection needle is properly protected in the transportation, storage and use processes, the probability of pricking injury is greatly reduced, the rubber column is fixedly installed at the front end of the outer needle cap, for old people with poor eyesight, a needle head can be inserted into the rubber column, and the rubber column is fixedly installed at the front end of the outer needle cap. The needle head can be hidden, and the problems that old people with poor eyesight are difficult to read metering scales, and a replaced injection needle is prone to hurting a user and other people are solved.

The invention relates to the technical field of intelligent control, in particular to an internet-of-things-based old-age care robot intelligent control system, which comprises a multi-mode sensing layer, a safety decision center, an execution driving module, a voice interaction optimization module and an internet-of-things communication module. According to the method, force sense data such as contact force of the mechanical arm, voice data such as voices of old people and view data such as obstacles are collected in real time through a multi-mode sensing layer, a displacement force coupling risk value is calculated through a hyperbolic tangent function coupling model of a safety decision center, and a deviation angle risk value is analyzed and calculated through an improved Lyapunov index spectrum; a total risk value is generated through fusion and is scheduled according to hierarchical intervals, mechanical operation, old people behaviors and environmental risks can be dynamically avoided, and the use safety is remarkably improved; through sub-ambiguity quantitative analysis, comprehensive ambiguity evaluation and intention correction of the voice interaction optimization module, instruction intention is optimized in combination with multi-modal data, the problem of fuzzy voice recognition of old people is effectively solved, and interaction accuracy and response efficiency are greatly improved.

The invention provides a voice interaction-based intelligent medicine use reminding system and method for the elderly department. The voice interaction-based intelligent medicine use reminding method comprises the following steps of: extracting phoneme characteristics of a target elderly in each historical dialogue round from each interaction voice; determining the pronunciation loss of the target old person in each historical dialogue round according to the phoneme similarity between the interactive voices in each historical dialogue round and the rhythm feature of each interactive voice, and further determining the semantic constraint condition of the target old person in each historical dialogue round; performing multi-intention recognition on the interaction voice of the target old person in the current dialogue round to obtain different semantic intentions of the target old person in the current dialogue round; further determining the semantic integrity of the target old person in the current dialogue round; and correcting the voice content of the target old person in the current dialogue round through the semantic integrity and the intention confidence of each semantic intention. By adopting the scheme of the invention, the fuzzy and distorted voice content of the elderly can be corrected in the voice interaction process.

The invention discloses a method for predicting an abnormal behavior state of an elderly living at home based on digital twinning, and the method comprises the steps: obtaining a social relation network data flow and a non-intrusive energy consumption data flow of the elderly living at home, and constructing a time sequence dynamic social relation graph based on the social relation network data flow, meanwhile, a semantic activity event chain is generated based on energy consumption data flow overload decomposition, cross-modal fusion processing is carried out on the social relation graph and the activity event chain, a composite state vector is generated, and based on a time sequence of the composite state vector, the social relation graph and the activity event chain are subjected to cross-modal fusion processing; and predicting the probability of entering a preset abnormal behavior state (such as social isolation, depression tendency or decline of self-care ability) of the home elderly. According to the invention, the privacy of the user is protected through non-intrusive data acquisition, and the social communication and exclusive activity information of the elderly are fused, so that the transformation from post-event alarm to pre-event early warning is realized, a precious time window is won for guardians or service institutions to take preventive intervention measures, and remarkable social and humanistic values are achieved.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention relates to an intelligent old-age care remote state monitoring method and device combined with a user portrait, and relates to the technical field of data processing, and the method comprises the steps: communicating a positioning patch array of a first old-age care user, and receiving a positioning time sequence matrix of a first time zone; processing a first pension user portrait and the positioning time sequence matrix through a health sample N-level adjacency searcher to obtain an adjacency sample state; aggregating the adjacent sample states to generate state baseline information; the communication state monitor is used for collecting state monitoring information of the first time zone; and when the state monitoring information does not belong to the state baseline information, performing abnormal state identification on the state monitoring information. The technical problem that in the prior art, due to the fact that flexible change analysis of the health value interval cannot be achieved, the monitoring accuracy of abnormal states of old people is insufficient is solved.

An electric height-raising toilet seat lifting aid includes a toilet seat cover, a bottom plate is fixedly provided on the toilet seat, a lifting mechanism is installed between the bottom of the toilet seat cover and the bottom plate, and the lifting mechanism includes an electric push rod that can be telescopic, a first starting rod and a second starting rod, the electric push rod is installed at the bottom of the toilet seat cover, the rear end of the first starting rod is movably connected to the rear end of the electric push rod, the front end of the first starting rod is hinged to the front end of the bottom plate, the front end of the second starting rod is rotatably connected to the front end of the electric push rod, the rear end of the second starting rod is movably connected to the bottom plate, the middle part of the first starting rod is hinged to the middle part of the second starting rod, and the rear end of the first starting rod is connected to the toilet seat cover, when the electric push rod is telescopic, it drives the first starting rod and the second starting rod to rise and fall, and then drives the toilet seat cover to tilt and rise. The invention has a simple structure and is easy to operate, solves the problem of inconvenience in raising and sitting on the toilet for the elderly, pregnant women and people with mobility difficulties, and has great social benefits.