15 results about "Gait disorders" patented technology

The invention discloses lower limb dyskinesia adjusting equipment and a gait adjusting method, and the equipment comprises a wearing part which comprises a housing matched with a lower limb; the projection module comprises a projector arranged on the shell, and the projection angle of the projector is adjustable; the control unit comprises a control mainboard arranged in the shell and an acquisition module communicating with the control mainboard, and the acquisition module is configured to acquire gait data of the wearing piece in real time; the projector communicates with the control mainboard, and the control mainboard is configured to control and adjust the projection angle of the projector based on the gait data acquired by the acquisition module. The lower limb dyskinesia adjusting device prompts gait disorders by using vision, and the mechanism is that the lower limb dyskinesia adjusting device can provide external sensory cues for the patient and stimulate the visual system of the patient, so that neural activity of the premotor area is activated. Visual information is used for guiding gaits, and the deficiency of internal movement rhythm caused by basal ganglion dysfunction is made up.

The invention discloses a gait recognition method for a Parkinson's disease patient, a medium and equipment, and the method comprises the steps: collecting gait video data of a user, inputting a trained gait recognition model, and outputting a Parkinson's disease classification result. According to the model, space-time gait features are extracted by adopting a backbone network, and the focusing capability on PD typical gait features is enhanced in combination with a multi-scale attention mechanism module; according to the method, time sequence dimensions are compressed through a horizontal pooling module, feature normalization and classification optimization are carried out through a BNNeck module, and triple loss and cross entropy loss combined training is adopted so as to improve the discrimination capability of the model on a PD gait mode. And finally, a fine-grained identity recognition result is converted into binary classification output of Parkinson's disease patients and non-patients through a secondary discriminator. According to the method, automatic detection of PD gait abnormity is realized, the recognition precision of the model on a PD specific motion mode is effectively improved, and early screening and auxiliary diagnosis of Parkinson's disease are facilitated.

The application relates to the technical field of cognitive disorder behavior monitoring, in particular to a cognitive disorder behavior monitoring and early warning method for Alzheimer's disease, which acquires gait parameters of each gait of an Alzheimer's disease patient, screens abnormal gaits, clusters the abnormal gaits based on distance measurement between any two abnormal gaits, obtains a plurality of class clusters, acquires gait disorder features of each class cluster, obtains gait turning features of each class cluster based on differences in disorder conditions between adjacent gaits in each class cluster, fuses the gait disorder features and the gait turning features of each class cluster, determines cognitive disorder behavior indexes of the Alzheimer's disease patient in combination with time intervals between adjacent class clusters, and performs early warning. Compared with a manual observation mode, the application comprehensively analyzes gaits of the Alzheimer's disease patient from multiple aspects, so that accurate cognitive disorder behavior monitoring results can be obtained.

The invention relates to a Parkinson's disease gait disorder training method, device and equipment and a medium. The training method comprises the following steps: a data acquisition step: acquiring gait data of left and right feet of a patient; step height calculation: calculating the step heights of the left and right feet of the patient according to the gait data; and a prompt generation step, wherein the patient is prompted according to the step height data obtained through calculation. Based on the height of the left and right feet of the patient, the vibration prompt is generated, the vibration prompt normal form set is constructed, the vibration prompt is dynamically adjusted according to the performance of the patient in the training process, and precise and personalized rehabilitation training is achieved.

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 present application relates to the technical field of gait training, in particular to a hanging rail gait training device and a training room. The hanging rail gait training device comprises a hanging rail, a fixing mechanism, a moving assembly, a hanging rope mechanism, a wearing mechanism and a controller. The hanging rail is of a spliced structure and comprises a straight section and / or an arc section. The modular hanging rail is spliced flexibly through the fixing mechanism to form multiple forms of hanging rails, such as a straight type, an S type and a closed loop type, thereby achieving full coverage and adaptation for patients with light, medium and severe gait disorders. Compared with a traditional large-scale device type gait training device, the device has low equipment cost investment and small size, can simulate turning and changing direction and other daily walking scenes, and the modular hanging rail does not need a large rigid frame structure, thereby greatly reducing the equipment investment cost. Meanwhile, multiple track forms can share the same training space, thereby significantly improving the utilization rate of a rehabilitation site, and the device is particularly suitable for a hospital rehabilitation department or a community rehabilitation center.

The invention discloses a functional auxiliary shoe, and aims to solve the problems of wearing and taking-off difficulty and falling risk of groups with limited limb movement, abnormal gaits or balance dysfunction. The auxiliary shoe is composed of a vamp system and a sole system in a cooperative mode, the vamp system is convenient to put on and take off and stably attached through a built-in heel cup, the heel cup is sequentially provided with an upper portion, an arch transition portion and a lower portion from a shoe opening to the inside, and in a side view, the arch transition portion at least partially protrudes out of the front portion, so that the heel is guided to smoothly slide in; the heels are clamped to prevent falling off when the shoes are worn; the shoe sole system adopts differential friction design, the first friction coefficient of the half sole area is smaller than the second friction coefficient of the heel area, the half sole low resistance reduces stepping jamming, the heel high resistance enhances the ground gripping stability, and the high resistance area extends to the outer side wall of the heel to prevent lateral slipping. Organic unification of wearing and taking-off convenience, walking stability and anti-skid safety is achieved, and the anti-skid shoe is suitable for wide groups such as old people, patients with nervous system diseases and patients with osteoarticular diseases.

The present invention relates to a system for transferring a patient into an appropriate position within a device and removing the patient from the device again by means of a crane system that supports the patient by compensating the patient's weight at a certain rate during physical therapy or exercise in which patients having a gait disorder or disability are made to walk on a treadmill. Fixed robotic locomotor therapy systems, by their structure, require the patient to be connected to an orthosis positioned on a treadmill and to have all or part of the patient's weight taken by a crane system in this position. Therefore, the patient must first be suspended from a crane system and connected to the orthosis. This situation creates the need to position the patient at a specific location on the treadmill.

The invention relates to the technical field of auxiliary detection of gait abnormity related diseases, in particular to an auxiliary disease detection method, system and device based on gait analysis and a medium, the method is suitable for gait abnormity related diseases, and the method comprises the following steps: collecting multi-modal gait data of a subject, and preprocessing the multi-modal gait data; gait features related to a pathological mechanism are extracted from the gait features; taking the gait features as structured nodes, determining a conduction driving association relationship among the structured nodes, and constructing an individual gait specific feature chain; aligning the individual gait specific feature chain with the structured nodes of a pre-constructed standard gait specific feature chain, and calculating the total matching degree score and the position matching degree of the structured nodes between the chains; and obtaining a risk assessment result of the target disease according to the total matching degree score and the position matching degree, and outputting a targeted intervention suggestion. According to the method, objectivity and pathological relevance of auxiliary detection are greatly improved, and scientific assessment of various gait-related disease risks is realized.

The invention relates to the technical field of cognitive impairment behavior monitoring, in particular to an Alzheimer's disease-oriented cognitive impairment behavior monitoring and early warning method, which comprises the following steps of: acquiring gait parameters of each gait of an Alzheimer's disease patient, screening to obtain abnormal gaits, clustering the abnormal gaits based on distance measurement between any two abnormal gaits, and obtaining a clustering result of the abnormal gaits; obtaining gait disorder features of each cluster, obtaining gait steering features of each cluster based on the difference of disorder conditions between adjacent gaits in each cluster, fusing the gait disorder features and the gait steering features of each cluster, and combining the time interval between the adjacent clusters to obtain a gait steering feature of each cluster. According to the method, the cognitive impairment behavior indexes of the patient with the Alzheimer disease are determined, early warning is carried out, and compared with a manual observation mode, the gait of the patient with the Alzheimer disease is comprehensively analyzed from multiple aspects, so that an accurate cognitive impairment behavior monitoring result is obtained.

This invention discloses a method, device, and medium for evaluating the efficacy of spinal surgery based on gait temporal analysis. The method includes: acquiring the coordinates of the toe, heel, and hip at each time frame of a walking video of a patient after spinal surgery; obtaining the hip anchor point coordinates based on the hip coordinate distribution, and dividing the gait into multiple gait cycles based on the heel and toe coordinate distribution; calculating propulsion symmetry and walking coordination based on the heel, toe coordinates, and hip anchor point positions within each gait cycle; further obtaining gait stability and coordination stability based on the propulsion symmetry and walking coordination distribution across multiple cycles, and determining the degree of recovery; and finally evaluating the postoperative efficacy based on the degree of recovery. Through gait temporal analysis, the method can keenly capture bilateral limb movement differences and subtle fluctuations during cycles, objectively quantifying the recovery status. This effectively solves the problems of strong subjectivity and difficulty in distinguishing similar gait abnormalities in traditional assessment methods, improving the accuracy and reliability of spinal surgery efficacy evaluation.

The invention discloses an intelligent insole and method for achieving monitoring and vibration feedback of child gait abnormity, the intelligent insole comprises an insole body and pressure sensors embedded in the insole body, and the pressure sensors are sequentially arranged on the inner side of a front sole, the outer side of the front sole, the inner side of a heel and the outer side of the heel and used for collecting pressure data of different areas of the sole in real time; the small vibration motors are correspondingly arranged and are used for generating local vibration feedback when the gait is abnormal; a wireless communication module is arranged in the middle area of the half sole of the insole body and used for transmitting plantar pressure data, collected by the pressure sensors, of different areas to an external mobile terminal through a microcontroller unit (MCU). An external mobile terminal transmits a control signal to a microcontroller unit (MCU) through a wireless communication module according to a preset logic so as to control a small vibration motor in a corresponding area to drive and generate a vibration feedback signal to remind a wearer of abnormal gaits; the device has the advantages of being quick in response, comfortable to wear and suitable for daily walking training.

The present application relates to the technical field of wisdom monitoring, and more particularly to an evaluation method and system based on angelman syndrome phenotype detection, which comprises the following steps: S1: obtaining video data, audio data, spatial positioning, image data and social interaction data of a target object; S2: preliminarily classifying and dividing a smiling event of the target object according to the video data, audio data, spatial positioning, image data and social interaction data, and obtaining a preliminary unprovoked smiling event of the target object. Through the preliminary and deep two-level detection mechanism, high-precision identification of the unprovoked smiling event is realized; based on multiple indexes such as step symmetry, gait variation coefficient and dynamic balance margin, a normalization and weighted aggregation strategy is adopted to provide an intuitive quantitative gait abnormality score, thereby providing an accurate reference for clinical evaluation.

The invention provides an intelligent wearing and AI combined clinical research data real-time analysis method and system, and relates to the technical field of AI.The method comprises the steps that gait data are collected through an intelligent wearing device, a dynamic graph structure is constructed, and multi-dimensional gait features are extracted through space-time graph convolution and a graph attention mechanism; recognizing gait abnormity by using a variational auto-encoder and a conditional random field model; and constructing a rehabilitation strategy generation model based on deep reinforcement learning, and dynamically optimizing a personalized rehabilitation guidance scheme. According to the method, accurate recognition and personalized rehabilitation guidance of the abnormal gaits are realized, and the clinical rehabilitation effect is improved.

The disclosure provides a continuous scale evaluation method and system for Parkinson gait disorder, and relates to the technical field of Parkinson gait analysis, which processes the gait skeleton sequence of a subject as follows: first, the gait skeleton sequence is converted into an upper limb feature map sequence and a lower limb feature map sequence, and then local spatio-temporal semantic features of the upper limb and the lower limb are extracted respectively; second, the local spatio-temporal semantic features of the upper limb and the lower limb are fused to obtain gait features; finally, the gait features are classified and regressed to obtain a coarse-grained classification vector and a continuous scale numerical score as a continuous scale evaluation result; the upper limb and the lower limb are separately modeled, and the self-attention mechanism is extended from the spatio-temporal dimension to the channel dimension, which enhances the ability of the model in capturing local spatio-temporal gait features, and finally, the flow type mixing technology and the linear regression method are used to realize the Parkinson gait disorder score in the continuous scale.