55 results about "Fall risk assessment" patented technology

The invention discloses a fall risk real-time assessment system and method based on big data, and relates to the field of risk assessment, and the system comprises a data collection and preprocessing module, a big data processing and integration module, a feature extraction and selection module, a fall risk assessment module and a real-time monitoring and early warning module which are sequentially connected and cooperatively work. Wherein the data acquisition and preprocessing module is responsible for acquiring data from a plurality of sources and converting the data into digital signals; the big data processing and integration module is responsible for secure transmission, convergence and preprocessing of data; the feature extraction and selection module is responsible for extracting features related to the falling risk from the preprocessed data; the tumble risk assessment module is responsible for real-time assessment of tumble risks based on the extracted features; and the real-time monitoring and early warning module is responsible for triggering an early warning signal according to the falling risk assessment result. According to the invention, accurate and real-time evaluation of the falling risk, accurate and timely output of the evaluation result and rapid response of the early warning signal are realized.

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 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 urban low-altitude landing risk assessment method based on three-dimensional space grids, and belongs to the technical field of low-altitude flight risk assessment. The objective of the invention is to solve the problem of accurate assessment of the low-altitude falling risk in a complex city scene. The method comprises the steps of performing three-dimensional rasterization processing on a low-altitude airspace of an evaluation airspace to obtain voxels of the evaluation airspace, and obtaining a voxel set of the evaluation airspace; building shelter formed by population density grids or mobile signaling inversion population, sensitive facility vectors, tree canopy shielding and three-dimensional building models is quantified, values are assigned to voxels of an evaluation airspace, and a voxelization environment parameter database covering the whole domain is obtained; constructing an aircraft failure rate model; carrying out an aircraft falling trajectory uncertainty simulation test to obtain landing coverage probability density distribution and tail end speed of the aircraft; and carrying out voxel risk synthesis by considering the probability of collision with people, and carrying out risk grading on the obtained voxel risk, thereby completing urban low-altitude landing risk assessment based on the three-dimensional space grid.

The invention discloses a metal mine fractured rock mass roadway roof fall dynamic risk assessment method, and relates to the technical field of mine engineering safety, and the method comprises the following steps: S100, carrying out the time-frequency spectrum data analysis of multiple monitoring points, constructing a phase synchronization deviation field, extracting the inherent frequency band characteristics of each monitoring point, and generating a resonance risk preliminary screening list; and S200, in a time window without external strong interference, progressive sweep frequency perturbation is applied to the monitoring points in the resonance risk preliminary screening list, amplitude change and phase lag data of disturbance response are recorded, an excitation coupling relation is identified based on an energy closure index, and dominant calibration of high-risk coupling points is completed. According to the invention, by constructing a phase synchronization deviation field, extracting a non-resonant robust signal, establishing a risk baseline and introducing an anti-verification and cross validation mechanism, effective elimination of false signals is realized; in combination with anti-phase disturbance and dynamic parameter adjustment, closed-loop enhanced control of the recognition model is completed, and the accuracy, responsiveness and prevention and control effects of mine roadway roof fall risk assessment are improved.

The invention relates to a tumble risk assessment method and system based on a multi-modal deep learning network, and relates to the technical field of human body posture recognition, and the method comprises the steps: collecting a scene motion image sequence, a plantar pressure feature sequence and personnel basic information; analyzing the scene motion image sequence by using a human body posture estimation network model to determine a human body key feature point sequence; performing feature extraction on the human body key feature point sequence and the scene motion image sequence to generate human body posture sequence features; performing feature extraction fusion on the human body key feature point sequence and the plantar pressure feature sequence to generate human body motion fusion time sequence features; performing feature extraction on the personnel basic information to generate personnel basic information features; and inputting the human body posture sequence features, the human body motion fusion time sequence features and the personnel basic information features into a multi-modal deep learning network for analysis so as to determine a fall risk score. The method and the device have the effect of improving the precision of fall risk assessment.

The invention discloses a tumble early warning system based on multi-dimensional feature fusion and a hierarchical trigger mechanism, and the system integrates the data of seven dimensions: traditional Chinese medicine diagnosis features (face image, eye diagnosis, tongue image, ear image and pulse condition), western medicine clinical indexes (electronic medical record and past medical history), posture behaviors, environment monitoring, inquiry semantics, voice voiceprint and psychological state. And accurate fall risk assessment is realized. The system adopts a three-stage trigger mechanism: a first-stage trigger module independently scores traditional Chinese medicine and western medicine features, and preliminarily quantifies the falling risk; the secondary trigger module independently scores the seven-dimensional features and outputs a multi-modal risk assessment result; and the third-level trigger module performs weighted fusion on the scores of all dimensions based on medical priori knowledge to generate low-risk, medium-risk and high-risk third-level early warning signals. Through multi-source data collaborative analysis and hierarchical decision making, the accuracy and clinical applicability of fall early warning are remarkably improved, and the method is suitable for old people health monitoring and medical aid decision making.

The invention discloses a wall surface falling risk assessment method, device and equipment and a storage medium, and relates to the technical field of building safety assessment, and the method comprises the steps: calculating a wall surface initial resistance value based on outer wall hyperspectral image data and a regression prediction model; calculating wall surface structure resistance according to the wall surface initial resistance value and a reduction coefficient corresponding to the wall surface initial resistance value, wherein the reduction coefficient is used for representing the inherent defect of the outer wall; simulating a structural load borne by the wall surface structure through a finite element agent model based on machine learning; and based on the structure load and the wall surface structure resistance, the wall surface falling risk probability is calculated through a Monte Carlo numerical simulation method by applying the structure reliability theory. The hyperspectral image can comprehensively capture the defect characteristics of the outer wall, and the initial resistance value is quantified by combining the regression model to supplement the traditional detection blind area. The reduction coefficient is used for associating inherent defects, the finite element agent model dynamically predicts the load to quantify the dynamic environment influence, and the comprehensiveness of wall falling risk identification is improved.

The invention discloses a falling risk assessment method and system based on a plantar pressure insole, equipment and a medium, and the method comprises the steps: obtaining plantar pressure data of a subject synchronously collected in a standardized TUG test process, and obtaining a clinical scale assessment result of the subject; the method comprises the following steps: preprocessing plantar pressure data of a subject, and extracting plantar pressure features and TUG time features; associating the extracted feature vector with a tumble risk tag evaluated by a subject scale, and constructing a data set with a tag; training a machine learning classification model by using the constructed data set; and inputting the feature vector of a to-be-evaluated subject into the trained classification model to obtain the fall risk level of the subject. According to the method, the falling risk can be objectively, quantitatively and comprehensively assessed in natural actions, clinical convenience and data scientificity are both achieved, TUG full-cycle biomechanical information can be completely captured in conventional environments such as communities and clinics, and a more accurate and interpretable assessment result is provided.

A caregiver assistance system helps caregivers manage the care of patients and the beds that support the patients. The system monitors tasks associated with the patients, such as caregiver rounding tasks, bed sore assessments, and / or fall risk assessments, and forwards completed tasks to an EMR server. The system also monitors the state of the beds and determines compliance with one or more healthcare facility protocols. Over time, the system gathers data from multiple beds regarding how often the beds are in compliance with the healthcare facility protocols and produces reports of the compliance levels to assist administrators in managing their healthcare facilities. The system may further monitor how quickly caregivers responds to one or more alert conditions relating to the beds and / or patients, and generate reports for the administrators regarding such response times.

The application provides a tree toppling risk assessment system and method based on a TOF depth camera, and the system comprises a TOF depth acquisition system, an edge detection module, a three-dimensional modeling module, an automatic identification module, a centroid estimation module and a risk assessment module. The application combines three-dimensional model reconstruction technology and mechanical principles based on a TOF camera, and adopts a specific formula algorithm to provide a new tree toppling assessment system and method, so that the tree toppling risk can be highly automatically assessed, the assessment efficiency and accuracy are improved, and the problem of a shortage of urban tree management professionals is relieved to a certain extent, and scientific basis is provided for prevention and decision-making.

The invention discloses a falling risk assessment method and device for a target object. The fall risk assessment method comprises the following steps: acquiring real-time monitoring video data (including multi-frame image data) associated with a target object; inputting the multi-frame image data into a target detection model to obtain human body key point coordinate data; performing target object state analysis processing on the human body key point coordinate data to obtain state data of a target object; in response to the condition that the state data indicates that the falling possibility of the target object is high, determining a first vertical speed based on a speed change rate between continuous frames in the multi-frame image data; in response to the fact that the first vertical speed is larger than or equal to a first preset speed threshold value, determining falling judgment data of the target object based on the frame image data of the preset frame number; and outputting a tumble risk assessment result indicating tumble of the target object in response to the tumble judgment data meeting a preset condition.

The present invention discloses an intercom-assisted rescue method and system based on fall detection in a rescue scenario, including: acquiring rescue status collection information and performing preprocessing, and constructing an operating environment data sequence, a communication status data sequence, and a motion status data sequence; constructing an anomaly detection model, inputting the motion status data sequence to analyze abnormal motion status data in real-time rescue tasks; performing fall detection on the target intercom user to determine whether there is a fall condition; if there is a fall condition, then combining the fall warning information to perform a fall risk assessment to determine whether collaborative rescue is required; if collaborative rescue is required, then search for the intercoms of other rescuers in the current communication environment for collaborative rescue, and perform communication optimization in combination with the operating environment data sequence and the communication status data sequence to expand the communication range. Thereby avoiding safety accidents caused by rescue personnel being unable to carry out independent rescue in case of emergencies during rescue operations.

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 embodiment of the invention provides a hospitalized patient falling risk assessment method, device and equipment and a storage medium, the method is applied to the technical field of hospitalized patient management, and the method comprises the steps: obtaining multi-dimensional assessment information corresponding to a target patient; the multi-dimensional evaluation information comprises evaluation information corresponding to basic daily life activity information, evaluation information corresponding to fall risk evaluation scale information and evaluation information corresponding to a medical advice medication list; inputting the multi-dimensional assessment information into a risk assessment model for risk prediction, and obtaining a target risk prediction result; generating a target nursing scheme based on the target risk prediction result and the multi-dimensional evaluation information; and managing the target patient based on the target nursing scheme. According to the embodiment of the invention, the falling risk assessment efficiency of the inpatient can be improved, and the falling occurrence rate of the inpatient is reduced.

The invention relates to the technical field of operation risk early warning, in particular to a visual perception-based high-altitude operation risk early warning system, which comprises an environment monitoring module, a personnel monitoring module, a behavior analysis module, a personnel risk assessment module, an object falling risk assessment module and an early warning generation module. According to the invention, by capturing the actions of the operating personnel in high-altitude operation in real time and identifying the behaviors such as inclination, hyperextension and unstable standing, early warning can be carried out in time when dangerous behaviors occur, the sensitivity and accuracy of early warning are improved, and by combining real-time heart rate and blood pressure monitoring with environment monitoring, risk assessment is more comprehensive, and the risk assessment efficiency is improved. Through real-time monitoring and distance analysis of the position of the object, assessment of the falling risk of the object is more accurate, accidents caused by falling of a tool or the object are effectively prevented, and through comprehensive consideration of various risk factors, an early warning strategy is adjusted in time, more targeted safety measures are provided, and the efficiency and effect of operation safety management are improved.

The invention discloses a tumble risk assessment method and system based on individualized information, and relates to the technical field of medical assessment and artificial intelligence, and the method comprises the following steps: a data acquisition step, a support period detection step, an individualized feature fusion step, a gait period segmentation step, a feature extraction step, and a time sequence coding step. A risk assessment step; firstly, multi-channel plantar pressure time sequence data of a subject and individualized information of the subject are obtained, and then through five-layer progressive processing, the problems of supporting period self-adaptive detection, gait cycle precise segmentation, multi-dimensional feature extraction, time sequence dynamic modeling and self-adaptive model training are solved respectively. And finally, accurate individual fall risk assessment is realized. Thresholds are automatically adapted for subjects with different physiological features, the gait deterioration trend caused by fatigue is captured through periodic-level double-flow time sequence modeling, and self-adaptive personalized feature learning is achieved through cascaded personalized information layer-by-layer fusion.

This invention discloses a method for assessing urban low-altitude fall risks based on a three-dimensional spatial grid, belonging to the field of low-altitude flight risk assessment technology. It aims to address the problem of accurately assessing low-altitude fall risks in complex urban scenarios. The invention includes: 1) 3) Rasterizing the low-altitude airspace of the assessment area into voxels, obtaining a voxel set; 2) quantifying population density grids or mobile signaling-derived population, sensitive facility vectors, tree canopy shading, and building shelters formed by three-dimensional building models, and assigning these values ​​to the voxels of the assessment airspace, resulting in a voxel-based environmental parameter database covering the entire area; 3) constructing an aircraft failure rate model; 4) conducting an uncertainties simulation experiment of the aircraft's fall trajectory to obtain the probability density distribution of the aircraft's landing coverage and terminal velocity; 5) considering the probability of collision with people, synthesizing voxel risks, and classifying the voxel risks to complete an urban low-altitude fall risk assessment based on a three-dimensional spatial grid.

The utility model discloses a detection device for predicting fall risk assessment of senile sarcopenia patients, which comprises a track action plate, the top of the track action plate is fixedly connected with two support plates, and a connecting plate is fixedly connected between the two support plates. Compared with a traditional device, by arranging the supporting plate, the track groove, the sliding plate, the connecting belt and the protection plate, corresponding protection measures can be conveniently provided according to the action of a patient; by arranging a semicircular plate, a first empty groove, a rack, a gear, a rotating shaft, an adjusting plate, a square groove, a square block, a threaded rod, a worm and a bracket, the angle and height of the bracket can be conveniently changed and adjusted according to armpit heights of different elderly patients, so that protection is provided when the elderly patients are detected, and the phenomenon of falling down is avoided; therefore, the safety is improved.

The application discloses a fall risk assessment method and system based on individual information, and relates to the technical field of medical assessment and artificial intelligence, and comprises the following steps: a data acquisition step, a support period detection step, an individual feature fusion step, a gait cycle segmentation step, a feature extraction step, a time sequence coding step, and a risk assessment step. First, multi-channel foot pressure time sequence data of a subject and individual information of the subject are acquired, then through five-layer progressive processing, problems of support period adaptive detection, gait cycle accurate segmentation, multi-dimensional feature extraction, time sequence dynamic modeling and adaptive model training are solved respectively, and finally precise individual fall risk assessment is realized. The application automatically adapts to the threshold of subjects with different physiological characteristics, captures the gait deterioration trend caused by fatigue through cycle-level double-flow time sequence modeling, and realizes adaptive personalized feature learning through layer-by-layer fusion of cascaded individual information.

Methods, systems, and computer-readable media are provided for improving patient safety using virtual observation. A falls risk assessment and a patient safety risk assessment are initially provided within an electronic health record of a patient. A clinician is prompted at a clinician device to provide input to the falls risk assessment and the patient safety risk assessment for the patient. Based on the input, a safety assessment score is determined for the patient. The safety assessment score is provided to the clinician via the clinician device and the clinician is prompted to initiate an order to place a camera in the room of the patient. Based on the order, a virtual sitter may be assigned to the patient to monitor the camera.

The invention relates to the technical field of smart home, and discloses a two-way call type home tumble recognition system based on remote control of the Internet of Things, which comprises a tumble recognition analysis module, a home equipment connection state acquisition module, a home equipment risk assessment module and a home equipment control module, the tumble recognition module comprises a tumble recognition judgment unit and a tumble risk assessment unit. According to the system, the falling condition of a user is comprehensively analyzed by reading a falling area of the user and analyzing and calculating to obtain an area weight coefficient, so that whether falling early warning needs to be carried out or not is judged, meanwhile, the system can give out emergency early warning during early warning, visual and auditory warning is carried out by controlling lamplight and loudspeaker equipment in a house, and the user experience is improved. The system can automatically cut off the circuit of the intelligent cooking equipment and close the gas valve of the internet of things, so that secondary damage caused by falling is prevented, and the intelligent household equipment is controlled in a linkage manner after the falling condition of the user is identified.

The invention discloses a fall risk early warning algorithm based on a diffusion model, and the algorithm comprises the steps: capturing human body gait data as a training data set through a non-contact motion capture device, and inputting the formed training data set into an algorithm model for training; the algorithm model comprises a human body gait prediction sub-algorithm and a fall risk judgment early warning sub-algorithm. According to the fall risk early warning algorithm based on the diffusion model, the gait is accurately predicted through the deep learning method, biomechanical interpretable prediction is achieved in combination with the reverse power part, the accuracy and the interpretability are combined, and early warning is more accurate. The method has the advantages of being high in generalization and stability, high in interpretability, accurate in prediction result and capable of advancing the early warning period.

The invention relates to the technical field of intelligent medical equipment, particularly discloses an intelligent kneecap falling risk assessment system based on multi-modal perception, and aims to solve the problems that a single sensor is limited in information dimension, and a traditional model is poor in individual adaptability and high in false alarm rate. The system comprises a multi-modal sensing module, a data preprocessing and feature extraction module, a spatio-temporal context fusion analysis module, a personalized dynamic risk assessment module and a decision output and early warning module. The comprehensive monitoring of the lower limb motion state and the accurate early warning of the falling risk are realized, and the individual adaptability and the alarm accuracy are effectively improved.

A caregiver assistance system helps caregivers manage the care of patients and the beds that support the patients. The system monitors tasks associated with the patients, such as caregiver rounding tasks, bed sore assessments, and / or fall risk assessments, and forwards completed tasks to an EMR server. The system also monitors the state of the beds and determines compliance with one or more healthcare facility protocols. Over time, the system gathers data from multiple beds regarding how often the beds are in compliance with the healthcare facility protocols and produces reports of the compliance levels to assist administrators in managing their healthcare facilities. The system may further monitor how quickly caregivers responds to one or more alert conditions relating to the beds and / or patients, and generate reports for the administrators regarding such response times. In some embodiments, the system may further provide instructions to caregivers for operating features of the bed and / or compliance with healthcare facility protocols.

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 present invention relates to a method for providing fall risk assessment by using a deep learning algorithm and a device therefor, the method being performed by a fall risk assessment device and comprising the steps of: loading data from at least one user terminal; pre-processing the loaded data; training a deep learning algorithm on the basis of the pre-processed data; inputting input data to the trained deep learning algorithm; calculating fall risk assessment data on the basis of output data output from the trained deep learning algorithm; and providing the calculated fall risk assessment data to the user terminal, wherein the fall risk assessment data is data classified into two categories of high risk and low risk with respect to a user's fall, and the step of loading data from the user terminal is loading data collected from two IMU sensors disposed on the user's body legs.

The caregiver assistance system helps caregivers manage the care of patients and the beds that support them. The system monitors tasks associated with patients, such as caregiver rounds, decubitus ulcer assessments, and / or fall risk assessments, and forwards completed tasks to the EMR server. The system also monitors the status of the beds and determines compliance with one or more healthcare facility protocols. Over time, the system collects data from multiple beds about how often the beds comply with healthcare facility protocols and generates compliance level reports that help managers manage their healthcare facilities. The system can also monitor how quickly caregivers respond to one or more alarm conditions related to the bed and / or patient, and generate reports on response times for managers. In some embodiments, the system can also provide caregivers with operational guidelines for operating bed features and / or complying with healthcare facility protocols.

The present disclosure provides a video game-based fall risk assessment system (100) designed for individuals (120) The system comprises sensor-equipped insoles (102) embedded in gaming slippers, featuring pressure sensors to monitor plantar foot pressure at key locations. An electronic control unit (104) processes this data and a video gaming application on a smart device (106) uses real-time pressure data to facilitate gameplay, evaluating skills relevant to fall prevention, such as balance and reaction time. A server (108) receives gameplay data, extract specific performance parameters (including reaction time and success rate) using a machine learning model (116), and analyze these parameters to assess the individual's balance and stability. The machine learning model (116) evaluates fall risk based on seated or a combination of seated / standing data, employing statistical analysis techniques. Finally, the system classifies individuals into "no / low risk of falls" or "high risk of falls" categories, considering posture-specific risks for accurate fall risk assessment.

A caregiver assistance system helps caregivers manage the care of patients and the beds that support the patients. The system monitors tasks associated with the patients, such as caregiver rounding tasks, bed sore assessments, and / or fall risk assessments, and forwards completed tasks to an EMR server. The system also monitors the state of the beds and determines compliance with one or more healthcare facility protocols. Over time, the system gathers data from multiple beds regarding how often the beds are in compliance with the healthcare facility protocols and produces reports of the compliance levels to assist administrators in managing their healthcare facilities. The system may further monitor how quickly caregivers responds to one or more alert conditions relating to the beds and / or patients, and generate reports for the administrators regarding such response times.