47 results about "Falling risk" patented technology

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 a multi-scene gait monitoring and motion function assessment method and system, a multi-camera cooperation system is deployed in a life scene to accurately identify an identity and extract multi-scene motion features, and an accurate old people gait function assessment scheme is provided through key motion hierarchical modeling, multi-scene data fusion and a risk suppression mechanism. Specifically, 2D human body key points are estimated based on an OpenPose algorithm, and a PoseLifter model is utilized to lift the 2D key points to 3D, so that a three-dimensional motion track of a target is estimated; dynamically adjusting the weights of the face features and the skeleton features during identity recognition according to ambient light; designing an independent long-short-term memory network for different actions to evaluate the motion function score and the fall risk rating of the actions; and designing a risk sensitivity mechanism and fusing multi-scene data to carry out comprehensive scoring. The gait function abnormity of the old people can be found in time, and a basis is provided for health management.

The present invention includes techniques for data processing and particularly relates to a method and system for predicting human fall risk based on electronic nursing text (ENT) data. The method comprises the following steps: obtaining an ENT data set, pre-processing data in the ENT data set, and constructing a Morse Fall Scale (MFS) dictionary with the pre-processed data; extracting features from the ENT data of patients to be predicted with a natural language processing technology; analyzing the extracted text features by the MFS dictionary to obtain a data set of risk factors; training a decision tree algorithm by using the data set of risk factors to obtain a prediction result of the patient's fall risk; clustering and precisely nursing the patients according to the prediction result. The invention constructs the MFS dictionary through the electronic health records to obtain fall risk factors of the patients, and iteratively predicts their fall risks according to the risk factors, thereby improving the prediction efficiency.

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 discloses a falling risk monitoring method and device, a storage medium and a computer program product. The method comprises the steps that sensor data collected by sensors located on a target object are acquired, and the sensors comprise a pressure sensor, a bending sensor and an inertial sensor; according to the sensor data, a climbing state corresponding to the target object is determined, and the climbing state is used for representing the climbing posture and the fatigue degree of the target object in the climbing process; according to the climbing state, a falling risk grade corresponding to the target object is determined, according to the falling risk grade, the operation state of a flexible guide rail anti-falling device is set, and the flexible guide rail anti-falling device is used for preventing the high-altitude operation personnel from falling. According to the method and the device, the technical problem that the robustness of climbing identification and falling early warning is poor due to the fact that climbing behavior identification mostly depends on image acquisition and analysis and is greatly influenced by the environment in the prior art is solved.

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.

A vestibular training device for improving vestibular function and balance and reducing fall risk of a subject may include a motion platform configured to support the subject and execute discrete motions, one or more input devices configured to enable the subject to provide responses to the motions, one or more feedback devices configured to provide the subject with feedback indicative of whether the responses are correct or incorrect, and a controller configured to cause the motion platform to execute a first motion, receive, via the one or more input devices, a first response indicative of the subject's perception of the first motion, cause the one or more feedback devices to provide first feedback indicative of whether the first response is correct or incorrect, determine a second motion based at least in part on the first response, and cause the motion platform to execute the second motion.

The invention provides a detection method and system for preventing wall falling, a medium and a product, which can comprehensively consider the difference between the personnel activity flow and the environmental interference factor and reasonably determine the falling detection period of a building wall. According to the method, the falling risk levels of the walls at different positions are divided according to the personnel activity flow data of the surrounding area of the building so as to express the probability that the walls fall off to cause danger to personnel; determining a first detection period of the wall body at different positions according to the falling risk level; adjusting the first detection period according to the historical environment interference data to obtain a final detection period; and based on the final detection period, performing falling risk detection on the walls at different positions. By dynamically adjusting the detection period, the method can focus on high-risk positions, optimize detection resource allocation, improve detection accuracy and efficiency, effectively reduce potential safety hazards, and realize scientific prevention and management of falling of the building wall.

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.

A fall risk estimation method comprising: a step for extracting a feature quantity of movement of an evaluation target portion of a subject from motion data of the subject when the subject is moving the evaluation target portion in a state in which the center of gravity of the subject is stable within a predetermined range; a step for calculating a variation index of the feature quantity; and a step for estimating a fall risk of the subject on the basis of the variation index.

Described herein is the design and implementation of an instrumented user device to produce quantitative metrics of patient fall risk. In some embodiments, a regression algorithm is used to estimate postural sway velocity, which is an effective predictor of fall risk. The instrumented user device enables continuous patient monitoring outside of the clinic and could provide a long-term, quantitative measure of fall risk.

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 invention relates to the technical field of medical treatment. The invention discloses a tumble prevention method, system and device and a storage medium, which can improve the applicability of the tumble prevention method and effectively prevent the occurrence of tumble events of hospitalized patients. The fall prevention method comprises the following steps: acquiring lower limb muscle force information, balance capability information and gait information of an inpatient based on a time sequence; generating a muscle strength grade based on the lower limb muscle strength information; generating balance curve information based on the balance capability information; inputting the muscle strength level, the balance curve information and the gait information into a fall risk prediction model for analysis and processing, and outputting a fall risk level; and generating first tumble early warning information based on the tumble risk level to prevent the hospitalized patient from tumble.

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 discloses a method and system for identifying fall risk levels. The method comprises the following steps: obtaining shoe slippage information, ground surface information, and obstacle information; calculating a slippage risk level indicator based on the shoe slippage information; calculating a ground abnormality risk level indicator based on the ground surface information; calculating an obstacle risk level indicator based on the obstacle information; and calculating a fall risk level based on the slippage risk level indicator and / or the ground abnormality risk level indicator and / or the obstacle risk level indicator. The present invention solves the problem of how to effectively analyze fall risk levels based on shoe slippage, ground abnormality, and ground obstacle conditions.

The invention discloses a community old people falling risk identification and intervention method based on machine learning, and relates to the technical field of old people falling risk identification, and the method comprises the steps: collecting the social economy, health condition, perceived environment and falling information data of old people by a system through offline questionnaire survey, and carrying out the data processing and statistics; based on the processed data, taking the tumble information data as a dependent variable, and utilizing a machine learning algorithm to build a tumble risk identification model; explaining the tumble risk identification model through an SHAP method to obtain a risk factor identification result; and based on a risk factor identification result, formulating a targeted anti-falling intervention measure for a corresponding community. Therefore, the method for identifying and intervening the falling risk of the old people in the community based on the machine learning is beneficial to finding the nonlinear relationship and the threshold value between the risk factor and the falling, so that different communities output targeted anti-falling measures for the old people.

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.

The invention discloses an old people falling risk assessment method and system based on human body posture estimation, relates to the technical field of computers, and solves the technical problem that old people falling risk assessment according to gait data is not accurate enough. And calculating and processing the gait features in the video frame, and obtaining an old people falling risk assessment result according to the gait features. No professional equipment or sensor wearing is needed, and the elderly can complete video shooting and risk assessment autonomously to adapt to family scenes. The elderly and family members have an opportunity to take corresponding prevention measures in advance, and serious consequences caused by falling down are avoided to the greatest extent; and the falling risk of the old people can be accurately classified and evaluated.

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.