22 results about "Falling risk" patented technology

The invention provides an old people falling risk monitoring analysis method and system, and the method comprises the steps: collecting multi-source heterogeneous signals, such as plantar pressure distribution, attitude trajectory output by an inertial measurement unit, electroencephalogram attention concentration ratio, galvanic skin response and the like, carrying out data synchronization and spatial normalization processing through a unified timestamp, and carrying out data synchronization and spatial normalization processing; extracting gait gravity center tracks and physiological state features; the gait deviation degree is matched by constructing an individual behavior baseline library and a dynamic time warping algorithm, the cognitive load state is evaluated in combination with a lightweight LSTM network, physical instability and cognitive risks are subjected to multi-dimensional fusion, a falling risk score is output through a fuzzy logic engine, and the system can achieve graded early warning response and emergency positioning. According to the invention, the accuracy, adaptability and early warning efficiency of fall risk assessment are significantly improved.

A fall risk determination device (40) is provided with: an acquisition unit (42a) that acquires a moving image showing a current person (1); an estimation unit (42b) that estimates, on the basis of the moving image, the skeleton of the pedestrian (1) reflected in the moving image; a classification unit (42c) that, on the basis of the skeleton, classifies a period during which the pedestrian (1) walks in the moving image into a plurality of periods; an extraction unit (42d) that extracts one or more first feature quantities relating to the walking of the pedestrian (1) on the basis of the skeleton for each of the plurality of periods; a determination unit (42e) that determines the risk of falling of the pedestrian (1) on the basis of the one or more first feature quantities; and an output unit (42f) that outputs the determination result of the determination unit (42e).

The invention provides a multi-source data fusion-based old people falling risk dynamic assessment method and system, and belongs to the technical field of health monitoring and intelligent wearable equipment. The method comprises an activity area calibration step, a fall risk identification step, a data acquisition mode updating step and a risk early warning step. The method comprises the following steps of: classifying an activity area into a high-risk area, a medium-risk area and a low-risk area, and updating a multi-source monitoring data acquisition mode of the area when identifying that the old people pass through the areas with different risk levels; performing fall risk identification based on the multi-source monitoring data acquired by the updated data acquisition mode; and when a falling risk is identified, a risk early warning signal is sent out. The system is used for implementing the method. According to the technical scheme, the activity area can be dynamically calibrated, the risk level can be accurately divided, the data acquisition mode can be adjusted according to the risk level, and the pertinence and timeliness of multi-source data fusion assessment of the falling risk are improved.

The present application relates to the technical field of electromyography, in particular to a multi-modal electromyography monitoring driven fall risk prediction method and system for the elderly. The method comprises the following steps: collecting electromyography sequence, comparing voltage changes to generate activity trajectory, calculating peak distribution based on the activity trajectory to generate discharge rhythm trajectory, extracting adjacent rhythm changes to generate change distribution record, comparing change position and time relationship to judge time continuity to generate change section record, and establishing association between change section record and gait cycle to generate risk prediction result. In the present application, dynamic trajectory is generated based on continuous sequence to extract microscopic rhythm characteristics, position adjacency relationship is verified during walking process to determine evolution continuity, continuous record is strictly compared with gait cycle to judge corresponding association, details of lower limb movement dynamic reflection are fully restored, defects of traditional truncation means covering lag phenomenon are effectively overcome, abnormal potential activity rules are accurately captured in complex scenes, and comprehensive reliability of risk prediction is effectively improved.

The invention provides an intelligent early warning and protection method and system for falling of an inpatient based on multi-modal data fusion. According to the method, individual risk data, behavior posture data and equipment state data of an inpatient are collected, a tumble risk index of the patient is calculated from the three aspects of potential risk, behavior risk and environmental risk by using a tumble risk prediction model, the tumble risk level of the patient is comprehensively evaluated, and then corresponding protection measures are taken. Specifically, each risk factor is quantified through a weight coefficient and a risk mapping function, and finally, an S-type normalization function is utilized to obtain a comprehensive risk index, so that accurate and personalized early warning is realized. The system comprises a sensing layer, a data processing layer, an intelligent analysis layer and an intervention execution layer, and can realize full-process automation and intellectualization. According to the invention, the problems of inaccurate evaluation, incapability of personalized early warning, lack of active intervention and the like in the prior art are effectively solved, and the occurrence rate of tumble of the inpatient is remarkably reduced.

This invention discloses an intelligent fall monitoring device, belonging to the field of fall monitoring technology, aiming to solve the problem of accuracy in fall monitoring and prediction for the elderly. It employs a collaborative artificial intelligence model architecture, integrating two different-dimensional artificial intelligence models for risk assessment. Model AI1, based on fuzzy logic, classifies fall risk levels into five levels: normal, low, medium, high, and emergency by real-time monitoring of vital signs such as heart rate, blood pressure, and blood oxygen saturation. Model AI2, based on deep belief networks, predicts risk by analyzing daily living activity patterns such as sitting, standing, walking, running, and jumping. The prediction results of the two models are input into a meta-model for comprehensive decision-making, generating the final low, medium, and high risk level prediction. For real-time fall monitoring, this invention proposes a hybrid monitoring algorithm, combining rule-based triggering based on acceleration and posture angle thresholds with AI fine-grained classification to achieve low latency and high accuracy.

The embodiment of the invention provides a quantifiable fall risk determination method, electronic equipment, a storage medium and a product, and relates to the technical field of data processing, and the method comprises the steps: obtaining a preliminary screening result of a fall risk of a target object, and obtaining a body function evaluation parameter, collected by timing equipment, of the target object; based on a preset quantitative scoring rule, calculating a body function scoring result of the target object; obtaining maximum angle range data, limb function quantitative data and gravity center displacement quantitative data of a specific body part of the target object in a preset motion test action; based on a preset weight coefficient and a quantitative fusion model, obtaining a quantifiable fall risk value of the target object; and determining the fall risk level of the target object according to a preset risk level division rule and the quantifiable fall risk value of the target object. The embodiment of the invention is used for providing a technical scheme capable of observably and accurately quantifying the falling risk of the old people.

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 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.

The invention relates to the technical field of data prediction, in particular to a tumble risk detection method based on multi-modal health data. For the feature data of the user, tumble risk prediction is carried out through an XGboost method, and a first tumble risk probability corresponding to the current moment is obtained; for the feature data of the user, the prediction accuracy of the large model is improved through an SFT fine tuning mode, and a second fall risk probability corresponding to the current moment is obtained; fusing the first tumble risk probability and the second tumble risk probability to obtain a comprehensive tumble risk probability; and taking the comprehensive fall risk probability as a prior probability in the Bayesian theorem, and obtaining a fall prediction probability through the Bayesian theorem. According to the method, multi-modal fusion and dynamic Bayesian updating are realized, the sign time sequence and the static health information are subjected to fusion analysis for the first time, the model robustness is improved, individualized risk tracking is realized, and the method adapts to health changes.

The application discloses an intelligent shoe capable of identifying falling risk of ascending and descending steps and an identification method, and the method comprises the following steps: calculating a step force risk assessment value of ascending and descending steps according to force information of a force foot when ascending and descending steps; calculating a step emptying risk assessment value of ascending and descending steps according to emptying information of an empty foot when ascending and descending steps; calculating a step alternation risk assessment value of ascending and descending steps according to switching information of the force foot and the empty foot when ascending and descending steps; and calculating a risk assessment value of ascending and descending steps according to the step force risk assessment value of ascending and descending steps and / or the step emptying risk assessment value of ascending and descending steps and / or the step alternation risk assessment value of ascending and descending steps, and judging whether there is falling risk when ascending and descending steps according to the risk assessment value. The application solves the problem of how to identify falling hazards caused by deviation of a force foot and an empty foot when ascending and descending steps, thereby effectively reminding personnel to avoid falling.

The application belongs to the technical field of fall risk prediction, and relates to a fall risk prediction method for the elderly based on multiple signal features, comprising the following steps: collecting surface electromyography signals, inertial signals and plantar pressure signals of a human body in real time, and performing time-frequency analysis on the surface electromyography signals to obtain a time-frequency spectrum of the surface electromyography signals; extracting feature vectors of corresponding signals in the same time period according to the signals and the time-frequency spectrum; obtaining a feature matrix by weighted fusion of the feature vectors; and inputting the feature matrix into a long short-term memory network model for processing, and outputting a fall risk probability.The scheme of the application can accurately output a fall risk probability by fusing multiple signal features, and can realize early prediction and real-time alarm, thereby preventing the elderly from falling.

The present application relates to the field of Chinese wisdom pension and old-age care, and discloses a fall early warning method and system for old-age care, which comprises the following modules: an old person individualized baseline modeling and system initialization module, a multi-source synchronous data acquisition and local preprocessing module, a multi-dimensional fall risk feature extraction and quantification module, a two-stage cascaded fall risk grading and pre-judgment module, a graded early warning and multi-linkage joint intervention module, and an event review and model dynamic iteration optimization module; and comprises the following steps: S1, a system initialization and baseline modeling stage; S2, a multi-source data acquisition and preprocessing stage; S3, a multi-dimensional risk feature extraction and quantification stage; S4, a two-stage cascaded risk pre-judgment stage; S5, a graded early warning and joint intervention stage; and S6, an event review and model iteration stage; the present application realizes pre-fall risk pre-judgment and intervention, reserves sufficient risk avoidance time for the old people, and reduces the occurrence probability of fall injuries from the source.

The invention discloses a working well platform personnel falling hidden danger early warning method, system and equipment, and relates to the technical field of falling risk monitoring. The method comprises the following steps: acquiring and preprocessing personnel-edge dynamic data, constraint effectiveness data and protection environment and process data, and respectively calculating an edge exposure index, a constraint effectiveness index and a protection environment and process index; dynamic weighted mapping is carried out based on the indexes to obtain a continuous risk score, a scene triggering threshold is combined to obtain a falling risk score, and the scene triggering threshold is deduced based on human kinematics and a falling protector braking mechanism; according to the falling risk score, graded early warning and linkage disposal are executed, prompt / alarm / emergency stop is triggered, and an event log is recorded. The method is used for solving the problems that the existing falling risk assessment is difficult to quantify the multi-source risk cumulative effect, and the response to typical high-risk scenes such as untied and hung, falling protector danger triggering and protection deficiency is insufficient.

A tipping risk detecting device (100) includes a first-attitude-angle information acquirer (101), second-attitude-angle calculator (102), attitude variation calculator (103), and tipping risk detector (104). The first-attitude-angle information acquirer (101) acquires first-attitude-angle information on a first attitude angle of a probe (1) in the inertial system. The second-attitude-angle calculator (102) calculates a second attitude angle of the probe (1) relative to the gravitational-acceleration direction of the astronomical object, based on the attitude-angle information, gravitational-acceleration direction relative to the inertial system, and probe's (1) shape information. The attitude variation calculator (103) calculates an attitude variation of the probe (1), based on the second attitude angles at the current time and preceding start time. The tipping risk detector (104) detects risk of the probe (1) tipping during its landing on the astronomical object, based on the attitude variation and positional relationship of a landed leg's (20) tip and the probe's (1) gravity center.

A computer-implemented method for assessing a fall risk of a user is provided. The computer-implemented method includes obtaining data indicative of the user engaging in a fall risk activity. The computer-implemented method includes adjusting a fall detection threshold of a wearable computing device worn by the user based, at least in part, on the data indicative of the user engaging in the fall risk activity. The method includes providing a notification indicative of the user being at risk of falling due, at least in part, to the user engaging in the fall risk activity.

The invention discloses an old people falling risk assessment system based on big data. The old people falling risk assessment system comprises a data acquisition module, a falling risk multi-dimensional feature screening module, an old people falling risk assessment model building module and an old people falling risk intelligent assessment module. The invention relates to the technical field of health data processing, in particular to an old people falling risk assessment system based on big data, innovatively introduces a norm sparse constraint feature screening objective function, and proposes a weighted divergence matrix reconstruction strategy based on a risk level weighted mean value. By combining a two-dimensional dynamic robust weight updating mechanism of a sample weight and a risk level pair weight, the stability and the reliability of a feature screening result are improved, redundant features are reduced, an improved clustering algorithm based on double-space collaborative mapping, a self-adaptive hyperbolic distance and a self-adaptive truncation distance is provided, and the clustering algorithm is suitable for being used in a large scale. The robustness and stability of the clustering algorithm are enhanced, and the accuracy of the output result of the old people falling risk assessment model is improved.

The invention relates to the technical field of electric power system early warning, and particularly discloses an electric power high-altitude operation falling risk dynamic assessment and early warning method and system, and the method comprises the steps: obtaining real-time personnel state data, equipment state data, environment data and operation flow data of a plurality of operation links in an electric power high-altitude operation process; extracting a risk factor corresponding to each operation link according to the real-time personnel state data and the equipment state data, and obtaining an initial risk feature vector of each operation link according to the risk factors; and dynamically correcting the initial risk feature vector according to the environment data and the operation process data to obtain a calibration risk feature vector corresponding to each operation link. Through multi-source real-time data acquisition (personnel state, equipment state, environment and process data), the problems of single dimension and poor timeliness of traditional assessment data are solved, and a comprehensive and dynamic data basis is provided for risk assessment.

This invention discloses a teaching method for intelligent simulation analysis of fall risk in elderly hypertensive patients based on machine learning, belonging to the field of fall risk simulation analysis technology. It includes: acquiring patients' frailty data to obtain a frailty dataset; classifying patients into at least three risk levels based on the frailty dataset, with each risk level corresponding to a different time folding coefficient, thus obtaining a frailty level classification item. This invention establishes a triangular assessment system of age, comorbidities, and cognition, and dynamically compensates for the underestimation of elderly or multi-disease patients by traditional models through a frailty adjustment coefficient, thereby improving the accuracy of high-risk patient identification. Furthermore, through intelligent time window compression technology, the monitoring period is automatically scaled based on the frailty level, shifting the golden window for patient intervention earlier, increasing the monitoring frequency for severely frail individuals, and improving the high-risk event capture rate.

An embodiment of the present invention relates to a method and apparatus for managing patient fall risk using SPPB test results, wherein the method and apparatus can predict the patient's fall risk based on SPPB test results and manage the patient's fall risk factors accordingly.

The application provides a two-dimension assessment method and system for the fall risk of the elderly based on a pressure foot pad, relates to the field of fall risk assessment, and comprises the following steps: collecting the plantar pressure distribution data of a subject under a test paradigm, and simultaneously collecting the cognitive confidence original score of the subject through an activity-specific balance confidence scale; extracting the pressure center coordinates and left-right foot load signals according to the collected plantar pressure distribution data, and calculating the balance function indexes; calculating the physiological ability total score through the index values in each function domain; simultaneously standardizing and converting the cognitive confidence original score to obtain the cognitive confidence score; calculating the comprehensive safety score, dividing the subject into multiple risk levels according to the comprehensive safety score, and outputting the assessment result; and the application constructs a two-dimension assessment system for physiological ability and cognitive confidence, carries out standardized scoring in combination with an age and gender norm, identifies the high-risk population of cognitive-ability mismatch type by using a difference index, and improves the accuracy and generalizability of the assessment.

The invention relates to the technical field of pressure detection, and provides an old man anti-falling system based on gait assessment, which comprises a data processor, and the data processor is used for executing the following processing strategies: detecting the plantar pressure of the old man in the walking process, and obtaining the gait instability degree of the old man in each single foot support period according to the change of the plantar pressure; according to the gait instability degree and the plantar pressure difference and the time interval difference between the adjacent single-foot supporting periods, the unbalance out-of-control degree of the old person is obtained; the related conditions of the gait instability degree and the walking speed change of the old people in the walking process are determined, and the response delay degree of the old people is obtained in combination with the change trend of the gait instability degree; the imbalance out-of-control degree and the response delay degree are fused to obtain the old man falling risk value, the accuracy of obtaining the old man falling risk value is improved, and then the accuracy and reliability of old man falling risk early warning are improved.