53 results about "Gait evaluation" patented technology

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

The invention relates to the technical field of knee joint rehabilitation evaluation, in particular to a knee joint rehabilitation evaluation method based on a human skeleton by using a space-time diagram convolutional network, and the method comprises the following steps: obtaining a rehabilitation evaluation gait video set; extracting human body key point skeleton data, and slicing the human body key point skeleton data into gait samples according to the number of time frames; preprocessing the gait sample, and extracting joint, skeleton and joint angle features; a gait-link method is adopted to divide a space gait skeleton diagram, a space-time attention mechanism is added, and an improved CTR-GCN network model is constructed; training the improved CTR-GCN network model by using the gait sample to obtain a gait evaluation model; and processing a gait video to be evaluated, and inputting the processed gait video into the gait evaluation model to obtain a rehabilitation evaluation result of the patient. According to the method, the human body key point skeleton in the walking video of the patient is extracted and input into the space-time diagram convolutional network for training, the rehabilitation evaluation model is obtained, the rehabilitation condition of the patient is evaluated by using the model, and the requirement of rehabilitation evaluation is met.

The invention discloses a standing and walking test method and system based on an inertial sensor and a myoelectricity technology, and relates to the technical field of gait evaluation and rehabilitation detection, and the method comprises the following steps: completing data acquisition by using the inertial sensor and the myoelectricity technology; comprehensively evaluating the difference between the left and right support phases and the swing phase through a first algorithm, and judging whether the gait is symmetrical and stable or not; and evaluating the muscle coordination of the patient through a second algorithm. According to the method, synchronous data fusion of the behavior layer and the neural control layer is achieved, a data foundation is laid for follow-up construction of an accurate and multi-dimensional gait and myoelectricity evaluation model, joint evaluation of two core indexes of gait symmetry and stability is achieved, the detection capacity of neurological gait disorders is improved, and the method is suitable for being used for the field of neurological gait disorders. Reliable criteria are provided for individualized rehabilitation intervention, modeling and difference measurement of time sequence regulation and control characteristics are achieved, and the recognition capacity for neuromuscular control disorders is improved.

The invention discloses a weight loss gait test evaluation and training device and method, and the device comprises a weight loss control module which is used for weighing the weight of a user in real time and automatically adjusting the air pressure of an air bag according to a set weight loss proportion; the gait information acquisition module is used for synchronously acquiring plantar pressure distribution, lower limb joint kinematics parameters and double-view video data; the multi-modal gait evaluation grading model module is used for processing plantar pressure distribution, lower limb joint kinematics parameters and double-view video data and outputting lower limb dyskinesia grades; and the training scheme generation model module is used for automatically generating a training scheme including a weight reduction proportion, a treadmill speed, single exercise time and training times per week according to the lower limb dyskinesia level and the personal information of the user.

The invention relates to the technical field of gait analysis, and discloses a Transform structure-based gait plantar pressure intelligent prediction system, which comprises a gait acquisition platform, a plantar pressure acquisition device, a gait kinematics acquisition device, a central processing server and a display terminal. The system obtains initial plantar pressure distribution through a lower limb multi-rigid-body chain type biomechanical model and foot-ground contact mechanics solution; and a gating prefix memory sliding window Transform is constructed in the central processing server, time sequence modeling and spatial correction are performed on the initial pressure matrix and the gait features, and a plantar pressure prediction result conforming to the biomechanical law is generated. The system effectively solves the problems that a traditional method is unstable in prediction, weak in generalization ability and lack of interpretability, plantar pressure prediction with higher precision is achieved, and the system can be used for gait evaluation, rehabilitation training and orthosis customization.

The invention discloses a child gait anomaly recognition feedback method and system based on plantar pressure data, and relates to the technical field of gait anomaly recognition, and the method comprises the steps: collecting the plantar pressure data of a target wearer through a sensor array, and carrying out the feature extraction of multi-dimensional gait feature parameters; comparing and analyzing the model with a child gait development standard model, and recognizing a gait abnormal mode; carrying out risk assessment on the target shoe wearer and dividing risk levels; performing gait analysis on the target wearer to generate an individualized gait analysis report; and performing gait correction analysis on the target wearer according to the individualized gait analysis report, and matching to generate a correction training course scheme. The technical problems that in the prior art, child gait evaluation lacks quantitative standards, detection intervention is disjointed, and personalized analysis based on a dynamic development model is lacked are solved, and the technical effects of achieving objective, accurate and intelligent recognition of child gait abnormity and improving the child gait health management level are achieved.

The invention provides a freezing gait assessment method, device and system. The frozen gait evaluation method comprises the following steps: generating a kinematics feature set, a dynamics feature set and a space-time parameter feature set of gaits of a detected object; for each feature set, constructing an attribute graph for representing the feature set, and performing feature extraction on the attribute graph to obtain a corresponding graph feature and an importance score of each gait feature in the feature set; based on the weight value corresponding to each feature set, carrying out fusion and feature extraction on the graph features corresponding to each feature set to obtain global features; inputting the global features into a prediction module to obtain a frozen gait severity score of the detected object; and for each gait feature, determining the gait score of the gait feature based on the normalized feature value of the gait feature, the importance score and the weight value corresponding to the feature set where the gait feature is located, and generating a radar map for representing the gait mode of the detected object based on the gait scores of all the gait features.

The invention discloses a multi-modal sensing data automatic labeling method and system for gait assessment, and the system comprises a sensing collection module which comprises two paths of pressure sensors and a six-axis IMU sensor; the two paths of pressure sensors are used for acquiring plantar pressure data; the six-axis IMU sensor is used for acquiring lower limb movement data; the wireless transmission module is used for uploading the collected plantar pressure data and lower limb movement data to a cloud Internet of Things platform; the cloud data processing module is used for receiving and storing the plantar pressure data and the lower limb movement data; the data labeling module is used for automatically recognizing a gait cycle and carrying out multi-dimensional score labeling; and the user interaction module is used for interacting with the data annotation module and the cloud data processing module and displaying gait evaluation related data and results. According to the invention, a multi-mode sensing PCB is self-developed, a closed-loop system of collection, cloud loading, labeling and display is constructed, and accurate, continuous and remote evaluation of gaits of children with cerebral palsy is realized.

The invention relates to the technical field of health status assessment, and relates to a gait assessment method and system based on multi-sensor data fusion, and the method comprises the steps: obtaining assessment data of a patient at a plurality of moments when the patient walks; calculating an acceleration characteristic value and a myoelectricity characteristic value at each moment; calculating a Pearson's correlation coefficient between the acceleration characteristic values at all moments and the myoelectricity characteristic values at all moments; and calculating a gait evaluation coefficient according to the Pearson's correlation coefficient and the accelerations of the plurality of target parts at all moments, and giving an alarm when the gait evaluation coefficient is smaller than a preset threshold. According to the invention, the evaluation result better fits the real physiological status of the patient, and the accuracy of gait evaluation is significantly improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

Provided is a gait evaluation support method executed by a computer, comprising: causing a display device to display a virtual reality (VR) environment simulating a living environment; acquiring data corresponding to walking action of a subject wearing the display device via a sensor and recording the acquired data; analyzing a gait state of the subject from the recorded data; and visualizing the result of the analysis so that the gait state of the subject can be evaluated.

The present application relates to the technical field of gait analysis, and discloses a gait plantar pressure intelligent prediction system based on a Transformer structure, which comprises a gait collection platform, a plantar pressure collection device, a gait kinematics collection device, a central processing server and a display terminal. The system obtains initial plantar pressure distribution through a lower limb multi-rigid-body chain biomechanical model and foot-ground contact mechanics solution; and constructs a gated prefix memory sliding window Transformer in the central processing server, performs time series modeling and spatial correction on the initial pressure matrix and gait characteristics, and generates plantar pressure prediction results conforming to the biomechanical law. The system effectively solves the problems of unstable prediction, weak generalization ability and lack of interpretability of traditional methods, realizes higher-precision plantar pressure prediction, and can be used for gait evaluation, rehabilitation training and orthosis customization.

The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.

The invention relates to a gait evaluation method and system based on three-dimensional simulation and an intelligent terminal, and relates to the technical field of gait evaluation, and the method comprises the steps: obtaining image detection information of legs of a patient; according to the image detection information, detection strip examination shape information of a detection strip preset on the legs of the patient is analyzed and determined; establishing a three-dimensional model on the basis of the detection strip inspection shape information to form leg simulation model information; analyzing change conditions among the leg simulation model information at different times to form leg change condition information; inputting the leg change condition information into a preset gait evaluation neural network model to form gait evaluation result information, and outputting the gait evaluation result information to a display terminal. The gait evaluation method has the effect of improving the accuracy of the gait evaluation result.

The application relates to the technical field of medical data processing, and discloses a non-walking type gait simulation evaluation system based on virtual reality interaction, which uses a virtual reality interaction module to make a to-be-tested person complete a gait task of simulated walking in any one or any combination of a cognitive load task, an emotional load task and a space obstacle task in a virtual reality test scene, so that real-time gait changes of the to-be-tested person in different scenes are simulated, efficient and accurate gait evaluation is realized in combination with a data acquisition module and a processor, the safety of the to-be-tested person in the gait evaluation process can be ensured, and the objectivity of the evaluation process can be ensured, especially in the case that the to-be-tested person often has a frozen gait, the time and frequency of footstep stagnation or change can be accurately recorded, doctors can accurately understand the occurrence mode of the frozen gait of Parkinson's patients, and effective data guidance is provided for personalized treatment of the patients.

The invention belongs to the technical field of gait assessment, and discloses a Parkinson's patient gait assessment method and related equipment, and the method comprises the steps: obtaining a radar point cloud and a micro-Doppler time-frequency spectrum when a user walks in a test area through a millimeter wave radar, and carrying out the preprocessing of the radar point cloud; a human body point cloud cluster is extracted from each frame of radar point cloud after preprocessing through a clustering method, and mass center position tracks of left and right feet are recognized in combination with Kalman filtering and a Hungary algorithm; calculating the speeds of the left and right feet according to the centroid position tracks of the left and right feet, and detecting step events of the left and right feet in combination with the micro-Doppler time-frequency spectrum; calculating gait evaluation parameters according to the time sequence of the gait events and the centroid point position of the human body point cloud cluster; comparing the gait assessment parameters with pre-stored reference data to generate a Parkinson disease assessment result of the user; therefore, continuous and convenient rehabilitation monitoring can be provided for the Parkinson's disease, and the accuracy and practicability of Parkinson's disease assessment are remarkably improved.

The present application belongs to the technical field of ankle rehabilitation, and relates to an interactive design method and device for an ankle-foot orthosis based on digital twinning, which comprises the following steps: obtaining structural data of an ankle-foot and an ankle-foot orthosis and establishing a three-dimensional model; obtaining real-time plantar pressure distribution data and mapping the data onto the three-dimensional model to form a digital twinning model containing dynamic behavior information; extracting plantar pressure features in the digital twinning model and using the plantar pressure features to construct a gait evaluation model and a gait anomaly detection model, the gait evaluation model being used to reflect the stability and pressure distribution of gait, and the gait anomaly detection model being used to detect abnormal gait in real time; when the gait evaluation model is lower than a preset threshold or the gait anomaly detection model detects abnormal gait, feedback information is generated and sent to the ankle-foot orthosis for parameter adjustment of the ankle-foot orthosis. The present application can make the ankle-foot orthosis better adapt to individual differences of users and provide personalized rehabilitation support for users.

The present invention relates to a telemedicine support system and, more specifically, to a telemedicine support system, which remotely measures physical functions of brain disease patients and the like who have difficulty in directly visiting a hospital without the help of a guardian, analyzes the measured physical data so as to perform accurate gait evaluation and the like, and performs telemedicine on the basis of the data such that a customized prescription can be provided to a patient.

According to an embodiment, provided is a method of estimating a gait speed, the method including: determining N subjects for gait speed measurement and obtaining personal data of the subjects; performing a clinical assessment on the subjects; obtaining measured gait data by performing a gait assessment on the subjects; and developing a gait speed estimation model having a plurality of gait factors based on the measured gait data, wherein the gait speed estimation model includes, as gait factors, a roll angle, a yaw angle, and a body weight measured by using an accelerometer worn on a center of body mass of the subject.

The application discloses a kind of children gait abnormality evaluation auxiliary systems based on human posture detection, comprising: video acquisition module, for obtaining video;Video contains gait image when child walks, and video does not limit shooting scene and shooting equipment;Human posture detection module, for detecting the human body detection frame of each human body object in video and the human body key point in frame using preset human posture detection algorithm;Human tracking module, for frame-by-frame matching the human body detection frame of each human body object in video, and according to the human body key point in matched human body detection frame, generate the continuous gait feature of human body object;Gait evaluation module, for comparing and analyzing the continuous gait feature of human body object with corresponding gait feature reference standard, and outputting the auxiliary evaluation result whether human body object exists gait abnormality according to analysis result.The application can accurately and efficiently evaluate children gait abnormality.

The application discloses a gait detection method, system and computer readable storage medium, and relates to the technical field of computers.The method comprises the following steps: performing attitude solution processing, filtering and standardization processing on acquired three-axis acceleration signals and three-axis angular velocity signals of a head to obtain a head motion signal sequence comprising gravity direction acceleration signals and gravity direction angular velocity signals; determining a frequency domain main frequency and a time domain energy based on the gravity direction acceleration signals, and further determining a gait candidate segment; detecting local maximum points of the gravity direction acceleration signals in the gait candidate segment, and taking the time length between two adjacent local maximum points as a gait cycle; detecting a gait starting event and a gait turning event according to the gravity direction acceleration signals and the gravity direction angular velocity signals; and calculating gait parameters according to the gait cycle, the gait starting event and the gait turning event, and further determining a gait evaluation result.

The application discloses a gait evaluation method and device, electronic equipment and storage medium, and relates to the technical field of gait analysis. In the application, gait data of a person with motor dysfunction in adjacent multiple walking cycles is obtained; the gait data comprises a gait phase parameter set and a posture angle parameter set of the person with motor dysfunction; from the gait phase parameter set and the posture angle parameter set included in the gait data, a gait phase parameter subset and a posture angle parameter subset corresponding to each of multiple gait evaluation dimensions are determined; based on the gait phase parameter subset and the posture angle parameter subset corresponding to each of the multiple gait evaluation dimensions, gait evaluation of the person with motor dysfunction is performed in the multiple gait evaluation dimensions to obtain multiple gait evaluation values; and based on the multiple gait evaluation values and gait evaluation weight values corresponding to the multiple gait evaluation values respectively, a comprehensive gait evaluation value of the person with motor dysfunction is determined. In this way, the gait data of the person with motor dysfunction is comprehensively quantified, and the accuracy of gait evaluation is improved.

The present application relates to the field of gait abnormality evaluation, in particular to a gait evaluation system and method based on a wearable sensor network, which comprises: collecting gait data of a user; classifying the gait data, including normal gait data and abnormal gait data; and scoring the classified gait data. The system and method objectively and accurately evaluate gait through an instrument. The lower limb gait data is collected by a data acquisition module, and the data is processed on this basis, and qualitative and quantitative evaluation of gait is performed, avoiding the direct experience and scale method in the prior art which relies on the subjective observation and scoring of testers, so that the evaluation is more objective.

The application discloses a standing-up and walking test method and system based on inertial sensors and electromyography technology, relates to the technical field of gait assessment and rehabilitation detection, and comprises the following steps: data acquisition is completed by using inertial sensors and electromyography technology; through a first algorithm, differences between left and right support phases and swing phases are comprehensively evaluated to determine whether the gait is symmetrical and stable; and through a second algorithm, muscle coordination of a patient is evaluated. The method realizes synchronous data fusion of a behavior layer and a neural control layer, lays a data foundation for subsequent construction of a precise and multi-dimensional gait and electromyography evaluation model, realizes joint evaluation of two core indexes of gait symmetry and stability, improves detection capability of neurological gait disorders, provides reliable criteria for individualized rehabilitation intervention, realizes modeling and difference measurement of timing control characteristics, and improves recognition capability of neuromuscular control disorders.

The application belongs to the technical field of gait assessment, and discloses a Parkinson's disease patient gait assessment method and related equipment, the method comprising: acquiring radar point cloud and micro-Doppler time-frequency spectrum of a user walking in a test area by using a millimeter wave radar, and preprocessing the radar point cloud; extracting human point cloud clusters from each frame of preprocessed radar point cloud by a clustering method, and identifying the mass center position trajectory of the left and right feet in combination with Kalman filtering and the Hungarian algorithm; calculating the speed of the left and right feet according to the mass center position trajectory of the left and right feet, and detecting the step event of the left and right feet in combination with the micro-Doppler time-frequency spectrum; calculating gait assessment parameters according to the time sequence of the step event and the mass center point position of the human point cloud cluster; comparing the gait assessment parameters with prestored reference data to generate a Parkinson's disease condition assessment result of the user; thereby providing continuous and convenient rehabilitation monitoring for Parkinson's disease patients, and significantly improving the accuracy and practicality of Parkinson's disease condition assessment.

The invention relates to the technical field of medical data processing, and discloses a non-walking type gait simulation evaluation system based on virtual reality interaction. A virtual reality interaction module is utilized to enable a to-be-tested person to complete a walking simulation gait task cooperatively generated with any one or any combination of a cognitive load task, an emotional load task and a spatial obstacle task in a virtual reality test scene so as to simulate real-time gait changes of the to-be-tested person in different scenes. Efficient and accurate gait evaluation is achieved by combining the data acquisition module and the processor, the safety of a to-be-tested person in the gait evaluation process can be guaranteed, the objectivity of the evaluation process can also be guaranteed, especially under the condition that the to-be-tested person often has frozen gaits, the time and frequency of footstep stagnation or change can be accurately recorded, and the accuracy of the gait evaluation is improved. A doctor is helped to accurately know the occurrence mode of the frozen gait of the Parkinson's patient, and effective data guidance is provided for personalized treatment of the patient.

The application discloses a kind of across patient phase identification and gait assessment method, belong to rehabilitation medical technical field.The method includes: the friction electricity signal generated when the lower limb of the patient to be measured is collected, obtains multimodal data;Multimodal data is used as input information, and gait assessment result and gait phase identification result are obtained by multitask model;Multitask model includes representation learning base network, unsupervised domain adaptation layer, mixed multitask layer;Representation learning base network extracts high-level features from multimodal data;Unsupervised domain adaptation layer obtains features by domain adaptation layer with weight parameter, and the features are mapped to a higher-dimensional latent feature space to obtain shared features;Mixed multitask layer inputs shared features into the independent subnetwork of multitask, and the gait assessment result and gait phase identification result of the patient to be measured are correspondingly output.The lower limb motor dysfunction patient is better to carry out rehabilitation training.

The invention discloses a mouse gait assessment method and related equipment based on deep learning, and the method comprises the steps: obtaining original video data, and carrying out the corresponding preprocessing, and obtaining to-be-processed data; detecting to-be-processed data according to the trained target detection model to obtain standard video stream data of three view angles, namely a look-up view angle, a left view angle and a right view angle; inputting the obtained standard video stream data into the trained deep learning model for feature extraction, and determining key point data of mouse movement in the video; performing data adjustment processing according to the key point data and preset parameters to obtain two-dimensional trajectory data under different viewing angles; and performing calculation according to the obtained two-dimensional trajectory data to obtain corresponding three-dimensional motion data, and performing index calculation according to the obtained three-dimensional motion data to determine an evaluation result of mouse motion. The embodiment of the invention can improve the evaluation accuracy. The method can be widely applied to the technical field of data processing.

The invention discloses a gait evaluation method, system and device, electronic equipment, a storage medium and a computer program product. The method comprises the steps that image data are acquired, and the image data comprise data of movement of a to-be-evaluated person in a detection area; performing feature extraction on the image data to obtain human body features of the to-be-evaluated person; based on a local covariance matrix, carrying out feature extraction on the human body features to obtain motion features of the to-be-evaluated person; and inputting the motion features into a first model to obtain a gait assessment result of the to-be-assessed person. According to the scheme provided by the invention, firstly, manual participation is not needed, full-automatic gait evaluation is realized, and the accuracy is high; secondly, the image data of the to-be-assessed person is convenient to collect, and the scheme is convenient to implement; and thirdly, the data volume of the motion features extracted by utilizing the local covariance is small, so that the calculation power and time cost of model analysis by utilizing the motion features are relatively low, and the evaluation efficiency is high.

The present invention discloses a contactless gait cycle feature analysis system, method and application thereof in screening of children with autism, including a contactless gait acquisition device and a feature analysis subsystem, wherein the gait acquisition device includes an acquisition path and a camera, which is used to collect videos and foot pressure of the person to be tested along the acquisition path; the feature analysis subsystem includes a natural gait acquisition module, an adaptive gait cycle detection module, a gait cycle standardization module, a gait feature extraction module and an abnormality assessment module; it is used to extract skeleton data of human movement according to the collected video and pressure value, and calibrate the data to perform gait cycle recognition and segmentation, and obtain gait features related to autism after height standardization, and realize disease assessment based on a machine learning algorithm. The present invention is simple to operate during use, has little restriction on the site, and is fast to collect, and can effectively assess the probability of autism through gait, and has important clinical application prospects.