265 results about "Functional disability" patented technology

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention relates to the technical field of cognitive impairment detection, and discloses a cognitive impairment early warning method and device based on an electroencephalogram micro state and an eye movement trajectory, and the method comprises the following steps: S1, data acquisition, S2, electroencephalogram preprocessing, S3, electroencephalogram feature extraction, S4, eye movement feature extraction, S5, feature fusion, and S6, early warning judgment. According to the method, through independent convolution branch of electroencephalogram and eye movement features, a receptive field is expanded by utilizing cavity convolution to capture multi-scale features, and long-distance dependence is modeled by a self-attention layer; after tensor splicing, time sequence information is dynamically fused through a gating cycle unit (GRU), and cross-modal time correlation is captured. The method has the advantages that the multi-modal feature hierarchical extraction and self-adaptive modeling capability is enhanced, the complementarity fusion efficiency is optimized, meanwhile, by means of cavity convolution sparse connection, self-attention parameter sharing and GRU lightweight design, the model complexity and the calculation efficiency are balanced, and efficient feature representation is provided for cognitive impairment early warning.

The invention relates to the technical field of medical rehabilitation, and discloses a virtual fusion personalized upper limb rehabilitation training cloud platform and method. The platform is composed of a multi-source biological signal acquisition module, a motion function feature modeling module, a heterogeneous data fusion module, a rehabilitation knowledge graph construction module, a dynamic training planning module and the like. The method comprises the steps of collecting multi-source biological signals, carrying out hierarchical feature modeling, carrying out data fusion, constructing an atlas database, generating a personalized rehabilitation scheme and the like. In this way, individuation and precision of upper limb rehabilitation training are achieved. Patient data can be comprehensively collected, fusion rehabilitation state representation is generated, a double-layer atlas database is constructed to assist training planning, training safety is guaranteed, the rehabilitation effect is predicted, pertinence, scientificity and efficiency of rehabilitation training are effectively improved, and high-quality rehabilitation service is provided for upper limb dysfunction patients.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The present application provides a comprehensive approach to improving cardiometabolic health and fitness through assessing key current health and fitness metrics, testing, and assignment of appropriate movement protocols that are specific to the needs of the individual. The present application provides a unified system for cardiometabolic health testing, activity and exercise monitoring, and user education and engagement. This platform may also be useful for the management of current health dysfunction and for the prevention of disease.

The invention discloses a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention provides an old people dyskinesia risk monitoring method, system and device and a medium, basic information of old people is acquired, a compliance evaluation model is established, and training parameters are adjusted based on a medical care manual intervention interface; identifying a high-risk factor combination by using an association rule, analyzing and predicting a movement function change trend of a future preset time length by combining a synchronous time sequence, and optimizing a decision weight by using a reinforcement learning module according to a medical care manual adjustment record; a dynamic risk assessment model is constructed by using a Bayesian network, cross-mechanism data cooperative training is realized by using a federal learning framework, time sequence features are extracted through feature engineering, and motion function indexes and risks of old people in a period of time in the future are predicted. And comprehensive evaluation, intelligent early warning and personalized intervention on the dyskinesia of the old people are realized. Rehabilitation training execution conditions of old people are monitored in real time, and rehabilitation prescriptions are dynamically adjusted according to execution data and subjective feedback, so that effectiveness of intervention is ensured.

The invention discloses a brain small vascular disease lower limb dysfunction self-adaptive electrical stimulation auxiliary rehabilitation system in the technical field of medical instruments, and the system comprises a data collection module which is used for obtaining the multi-modal data of a patient in real time; the decision regulation and control module is used for generating dynamically optimized electrical stimulation parameters and rehabilitation task instructions based on a deep reinforcement learning algorithm and patient individualized pathological characteristics according to the multi-modal data; the electrical stimulation execution module is used for synchronously regulating and stimulating nerve targets according to the optimized electrical stimulation parameters; and the interactive virtual reality module is used for combining immersive gait training and cognitive training. By detecting the multi-modal physiological data of the patient, combining the deep reinforcement learning model to dynamically optimize the electrical stimulation parameters, and coupling VR exercise training and cognitive training, the three-in-one collaborative rehabilitation mode of electrical stimulation-physical treatment-cognitive intervention is realized, the rehabilitation training efficiency is improved, the relapse risk of the patient is reduced, and the rehabilitation training experience of the patient is enhanced.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses a mild cognitive impairment auxiliary recognition system and method based on eye movement characteristics, and relates to the technical field of computer-aided diagnosis, and the system comprises a data obtaining module which is used for obtaining eye movement data of a patient in the emotion classification task process; the data preprocessing module is used for performing data preprocessing on the acquired eye movement data; the auxiliary recognition module is used for inputting the preprocessed eye movement data into an auxiliary recognition model based on a double-flow fusion network, the auxiliary recognition model comprises a spatial feature extraction module, a time sequence feature extraction module and a multi-modal fusion classification module, key features of the eye movement hotspot map are extracted through the spatial feature extraction module, and the key features of the eye movement hotspot map are extracted through the time sequence feature extraction module; the time sequence features of the dynamic eye movement sequence are extracted through the time sequence feature extraction module, the features extracted by the two feature extraction modules are spliced and fused through the multi-modal fusion and classification module, and final accurate classification and recognition results of the Alzheimer's disease, the mild cognitive impairment and the health are output.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention relates to the technical field of intelligent control, and discloses a power-assisted control method of a lower limb pneumatic flexible exoskeleton and a lower limb pneumatic flexible exoskeleton system.The method comprises the steps that healthy side plantar pressure data are obtained, healthy side key gait event nodes are recognized, and the power-assisted control method of the lower limb pneumatic flexible exoskeleton is obtained according to the healthy side key gait event nodes and ankle joint angle data; estimating an uninjured side continuous gait phase, and calculating an affected side target gait phase according to the uninjured side continuous gait phase; according to the target gait phase of the affected side, auxiliary torque and tension are determined; according to the auxiliary torque and the pulling force, target air pressure for supporting the airbag and shrinking the airbag is determined, an electromagnetic valve is controlled to execute deflation or inflation operation according to the target air pressure, and the internal pressure of the airbag is controlled within a target range. The invention aims to provide flexible assistance conforming to the biomechanical characteristics of the ankle joint of a human body for people with ankle joint dysfunction, improve the walking ability and improve the safety and comfort of rehabilitation training.

The invention discloses a prediction model construction method for rheumatoid arthritis accompanied with brain cognitive impairment, and belongs to the technical field of medical assistance, and the method specifically comprises the steps: synchronously collecting multi-source dynamic behavior data when a patient executes standardized joint activity training each time; vector superposition comparison under a three-dimensional coordinate system is carried out on the joint motion trajectory data, and the trajectory offset degree of each motion phase point is calculated; fractal dimension calculation is carried out on the moving path topological structure data, and path complexity is quantized; performing feature matrix construction on the facial micro-expression data, and calculating covariance structure change of an expression parameter matrix; and performing time domain alignment and causal analysis on the spatio-temporal trajectory offset event, the path topology degradation characteristic and the expression parameter variation signal, and when the three types of abnormalities present time coupling in two continuous training periods and meet a preset condition, generating early warning of cognitive impairment.

The utility model relates to the field of medical auxiliary devices, in particular to a cognitive function rehabilitation training device for the neurology department, which comprises a mounting cabinet, one hundred groups of pressing assemblies, a display screen, an expression lamp assembly, a visual detection sensor, a microphone, a card box assembly and an electric control unit, the pressing assembly is provided with number buttons and an indicating lamp, and each number button is marked with a number selected from 0 to 99; the expression lamp assembly comprises a pleasant expression lamp and a depression expression lamp; the card box assembly is provided with a card temporary storage box and a plurality of card storage boxes; the visual detection sensor can collect digital information on the extracted digital cards. According to the utility model, the cognitive function of a patient is trained by pressing with fingers for many times, judging with the brain, speaking with the mouth and the like, and the interestingness is enhanced by feeding back whether the expression of the expression lamp assembly is accepted or not, so that the patient is pleasant to accept training to promote the rehabilitation of cognitive impairment in multiple aspects of the patient.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching enginethat exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention relates to the technical field of artificial intelligence and big data, and discloses a learning disorder assessment and adaptive training system and method based on multi-dimensional behaviors, and the system comprises a multi-modal assessment module, a path generation module, a nerve regulation and control module and an adaptive engine module. By fusing multi-modal data features of language behaviors, electroneurographic signals and task response behaviors and combining a cross-modal attention mechanism to dynamically construct a cognitive state recognition model, learning disorder typing accuracy is improved, multi-dimensional ability defects such as attention, memory, language understanding and execution control are quantified based on a three-dimensional cognitive map, and learning disorder typing accuracy is improved. It is ensured that the assessment result comprehensively covers the core dimension of cognitive impairment; transcranial direct current stimulation is synchronously triggered in the cognitive task starting stage, space-time precise matching of task state nerve regulation and brain region activation is achieved, stimulation target point coordinates and current intensity are dynamically adjusted according to real-time nerve feedback, and the neuroplasticity reconstruction efficiency is improved.

A novel, wearable therapeutic medical device that seamlessly integrates myofascial release therapy technology into daily life by discreetly targeting trigger points and otherwise dysfunctional tissue in order to relieve associated pain, discomfort, stiffness, and tension. Individuals wearing this novel therapeutic medical device are capable of continuing to go about their everyday activities while effortlessly improving their musculoskeletal health.

The invention discloses a mental disease brain dysfunction detection method based on dynamic causal modeling, and belongs to the field of biomedical engineering. The dynamic causal relationship of information transmission among regions of the brain can be accurately captured by adopting dynamic causal modeling based on electroencephalogram, and the method has unique advantages in the aspect of revealing abnormal modes of mental disease information transmission. Different from a traditional functional brain network construction mode, the system pays more attention to capturing the causal relationship and dynamic change of brain intervals when processing and explaining data, the modeling method based on the causal relationship can provide more detailed and personalized information transmission analysis to a certain extent, a more accurate result is provided, and the method is suitable for popularization and application. Furthermore, the functional disorder in the mental disease brain neural network can be sensitively recognized, the pathological mechanisms of bipolar affective disorder, schizophrenia and split affective disorder can be revealed, and support is provided for early diagnosis and personalized treatment.

The invention discloses a daily life activity ability assessment method and system based on a multi-branch space-time fusion network. The method comprises the steps of collecting motion data of a to-be-evaluated object, performing segmentation, noise reduction and standardization preprocessing, and extracting time-frequency domain features and motion deviation degree, motion similarity index and bilateral symmetry index provided by the invention; obtaining a uniform Bhattacharyya index scale score; and taking the motion feature data set and the clinical score as input, performing training based on a multi-branch space-time fusion algorithm, constructing a multi-task prediction model, and realizing automatic evaluation of the ADL. According to the method, the multi-branch space-time fusion network is innovatively introduced, the relevance between tasks is fully utilized, the spatial feature extraction capability is enhanced in combination with the graph neural network, adaptive fusion of the features is realized through a cross-layer interactive attention mechanism, the model prediction precision and evaluation reliability are remarkably improved, and the method is suitable for large-scale popularization and application. And an effective technical support is provided for rehabilitation evaluation and personalized treatment of patients with dyskinesia and disability and dementia.

The invention provides a schizophrenia early recognition and risk prediction method and system based on multi-mode speech analysis, and belongs to the technical field of medical artificial intelligence. The method comprises the following steps: prospectively acquiring natural voice data of an individual in a clinical high-risk period, performing clinical follow-up visit for two years, and constructing a first Chinese schizophrenia clinical high-risk voice longitudinal data set; based on the data set, audio and text features in the voice are extracted, a layered multi-mode integrated model is constructed, semantic coherence and acoustic features are fused, and early prediction of the schizophrenia transformation risk is achieved; through feature importance analysis, key voice marks closely related to negative symptoms, thinking disorder and cognitive impairment are recognized, and non-invasive, accurate and interpretable early recognition is achieved. According to the method, the technical blank of early screening tools for schizophrenia in Chinese context is filled, and the accuracy and stability of early recognition are remarkably improved.

The invention discloses a cerebral infarction swallowing dysfunction risk prediction method based on machine learning, and belongs to the technical field of risk prediction, and the method comprises the following steps: S1, retrieving and collecting a plurality of data sets related to cerebral infarction from an MIMIC IV database; s2, original data in the data set are preprocessed, clinical information is strictly verified, and data which does not conform to research standards are removed; s3, predicting the processed data through a plurality of models, and finding out the model with the best performance; and S4, continuously training the optimal model, constructing a swallowing function risk prediction model of the optimal model, deploying the optimal model to a webpage based on a Streamline framework, predicting the swallowing disorder risk of the patient, and generating a personalized SHAP force field graph. According to the method, a plurality of machine learning models are constructed, the model with the optimal performance is selected, and the cerebral infarction dysphagia risk is accurately predicted in combination with clinical data.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of SCN2A RNA in a cell or subject, and in certain instances reducing the amount of SCN2A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a disease or disorder associated with a voltage-gated sodium channel protein, such as, for example, a Developmental and Epileptic Encephalopathy, an intellectual disability, or an autism spectrum disorder. Such symptoms and hallmarks include, but are not limited to seizures, hypotonia, sensory integration disorders, motor development delays and dysfunctions, intellectual and cognitive dysfunctions, movement and balance dysfunctions, visual dysfunctions, delayed language and speech, gastrointestinal disorders, neurodevelopmental delays, sleep problems, and sudden unexpected death in epilepsy.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

Methods and compositions for treating social function disorders are disclosed. The methods involve administering compound of Formula I

1. Name of the product in this design: Cognitive Impairment Assessment and Training Equipment. 2. Purpose of this design: For the assessment and training of mild cognitive impairment. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This utility model discloses a rehabilitation device for lower limb dysfunction with adjustable angle, relating to the field of rehabilitation technology. It includes a base frame and a computer operating unit. The computer operating unit is installed on the top of the base frame, which has an adjustable support unit on top and a body support unit on top of the adjustable support unit. The device utilizes four square blocks on both sides inside the foot pedal. Two of the blocks have Velcro fasteners (first type) on top, and the other two have Velcro fasteners (second type) on top. During use, the patient places their foot on the foot pedal, and the Velcro fasteners (first and second types) securely fix the foot to the pedal. This effectively prevents the foot from slipping or becoming unstable on the foot pedal due to weakened muscle strength or poor balance during rehabilitation training, thus ensuring the smooth progress and safety of rehabilitation training.

The invention relates to the technical field of intelligent monitoring of mental health, and discloses a social function prediction system for teenager patients with depression. The system comprises a multi-source data acquisition module, a psychological feature fusion module and a prediction engine module. The multi-source data acquisition module is used for acquiring social interaction records of patients, constructing behavior dynamic characteristics, performing mode recognition and generating behavior mode data. And the psychological feature fusion module performs difference comparison on the behavior data and a healthy group benchmark, screens key behavior indexes, and calculates correlation degree values of the key behavior indexes and psychological assessment parameters in real time. The prediction engine module evaluates the social function degradation type based on the correlation degree, locates vulnerable nodes in the development process, and identifies specific dysfunction points. According to the system, continuous dynamic monitoring of social function states is realized, early warning of degradation risks and accurate positioning of weak links are realized through behavior-psychological correlation analysis, and technical support is provided for targeted intervention.

The invention relates to the technical field of medical instruments, and discloses a child critical patient nursing restraint device which comprises a supporting mechanism, the supporting mechanism comprises a supporting frame, supporting legs are fixedly connected to the outer wall of the bottom of the supporting frame, and a first hydraulic rod is fixedly connected to the outer wall of the bottom of the supporting frame; the inner wall of the end, close to the first hydraulic rod, of the supporting frame is fixedly connected with a first connecting shaft, and the adjusting mechanism comprises a second sliding frame fixedly connected with the output end of the first hydraulic rod. The deflection angle of the turnover frame is adjusted through the second motor, so that the contact area between the air bag in the turnover frame and the critical child patient is increased, and peripheral circulation dysfunction caused by long-term too tight compression or improper binding angle of local tissue is avoided.

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.