42 results about "Motor behavior" patented technology

The invention discloses a quadruped robot motion control method and system based on reinforcement learning, and the method can simulate and analyze the motion behavior of a quadruped robot in a virtual environment through building a simulation model of the quadruped robot, remarkably reduces the research and development cost, remarkably shortens the research and development period, converts a balance control problem into a Markov decision process, defines a state, an action and a reward function, and improves the reliability of the quadruped robot. A more intelligent and adaptive balance strategy can be realized, a deterministic strategy gradient algorithm and a simulation model are adopted for interactive training, an efficient balance control strategy can be obtained, robot behaviors are adjusted in real time to keep balance, a walking task is decomposed into two sub-tasks of foothold selection and gait generation by utilizing a hierarchical control idea, and the walking efficiency is improved. Through reinforcement learning, walking flexibility and stability of the robot on irregular terrains are improved, the irregular terrains are built on a simulation platform for experimental verification, robot performance can be safely and repeatedly evaluated, and a strategy is further optimized.

The invention discloses a portrait construction method for a disability risk population, and an exercise training recommendation method and system, and the method comprises the following steps: determining portrait mapping key elements according to a pathological mechanism; constructing a core portrait based on the key elements; constructing a basic portrait according to the demographic information, the exercise investigation information and the exercise behavior characteristics; constructing a three-dimensional character portrait based on the core portrait set and the basic portrait set; wherein the key elements comprise cognitive cortex injury, body function injury and emotional control force injury; the attributes of the core portrait comprise cognitive cortex injury, body function injury and emotional control force injury, and the tag data is derived from diagnosis type information of a patient; the exercise survey information includes exercise preferences. According to the method, an exercise training scheme which is low in exercise risk, high in exercise efficiency, high in compliance and capable of dynamically iterating is provided for exercise prevention and repair of people with high disability risks.

The invention provides a personalized intervention strategy generation method driven by parent-child interaction type motion data. Through a multi-source fusion perception technology, motion tracking, bioelectricity sensing, environment sensing and social interaction data acquisition are integrated, and parent-child motion behaviors, physiological indexes, psychological emotion and environment data are comprehensively collected. A deep learning correlation analysis model is used for mining a complex nonlinear relationship among multi-source data, and parent-child groups are dynamically divided according to real-time data by means of a dynamic clustering evolutionary algorithm. And based on an evaluation result, customizing an exclusive exercise scheme by using a VR / AR technology, and constructing a real-time adjustment strategy of the emotion intelligent feedback system. Besides, through an adaptive learning adjustment engine and a community interaction and strategy sharing platform, an intervention strategy is dynamically optimized according to exercise data changes and user feedback, parent-child exercise experience is improved, and parent-child relationship and health level are promoted.

The invention discloses a rehabilitation exercise risk assessment method and device, and relates to the technical field of exercise rehabilitation, and the method comprises the steps: building a body load baseline according to user basic information and static medical history, collecting the exercise posture data of a user through a wearable device, and determining and triggering exercise risk monitoring through three-level abnormal event judgment; and backtracking physiological parameter data, establishing a real-time state vector, comparing the real-time state vector with a body load baseline, outputting a real-time motion risk coefficient, matching a real-time grading intervention strategy, outputting a self-adaptive intervention instruction after an ultra-short-term alarm log is corrected, and driving motion behavior intervention correction. The technical problems of one-sided data and insufficient accuracy caused by the fact that a traditional rehabilitation exercise risk assessment method does not integrate dynamic exercise postures and real-time physiological parameters are solved, accurate and comprehensive assessment, graded intervention and closed-loop tracking of rehabilitation exercise risks are achieved, the exercise injury probability is effectively reduced, and the rehabilitation exercise risk assessment efficiency is improved. And safety and effectiveness of rehabilitation of the patient are guaranteed.

The invention discloses a video analysis-based early warning and monitoring method for children hyperactivity behavior and a storage medium, and the method comprises the steps: firstly, guaranteeing the comprehensive monitoring of children behaviors through employing a multi-channel video collection device, and providing a sufficient continuous data source for subsequent analysis; furthermore, the quality of monitoring data is ensured through the use of image noise reduction and segmentation technologies, and accurate motion features are effectively extracted through the combination of an optical flow algorithm and a background subtraction algorithm; further, the movement behaviors of the children are comprehensively analyzed by calculating behavior parameters such as movement amplitude, movement direction change and continuous movement time, and potential behavior abnormity is revealed; the multi-dimensional behavior feature vector obtained through behavior parameter analysis is compared with the standard template, the behavior type of the child is automatically recognized, early-stage symptoms of abnormal behaviors such as hyperactivity and the like are found in time, and a basis for early diagnosis and intervention is provided for behavior problems such as hyperactivity and the like of the child.

This invention discloses an individual motion behavior analysis system based on dynamic modal configuration and rule-guided fusion, belonging to the fields of intelligent perception, sports science, and human-computer interaction. It includes a data input module, a dynamic modal organization and configuration module, a multimodal feature modeling and fusion module, an indicator-level evaluation and behavior state prediction module, a statistical rule-guided analysis and result correction module, and a structured result generation and output module. These modules work together to form a complete closed loop for motion behavior analysis technology. This invention aims to address the problems in existing motion behavior analysis technologies, such as limited dimensionality of single-modal information, rigid multimodal fusion methods, insufficient stability and interpretability of analysis results, and strong dependence on large-scale manually labeled data.

This invention relates to a precise EEG decoding method and brain-computer interface system based on evoked brain activity, comprising: an EEG acquisition module for acquiring raw EEG signals in resting and motor states; an EEG preprocessing module; a brain dynamics model that, using the purified resting-state and stimulated-state EEG signals processed by the EEG preprocessing module as input, obtains the internal states of spontaneous brain activity and the mixed internal states of spontaneous and evoked brain activity, thereby obtaining the internal states of evoked brain activity; an internal state-EEG conversion network that, using the purified signals processed by the EEG preprocessing module as input, obtains reconstructed EEG signals using an encoding-decoding network; a training module that simultaneously optimizes the model parameters and the internal state-EEG conversion network parameters; and a decoding module that decodes motor behavior based on the internal states of evoked brain activity output by the brain dynamics model. This improves the accuracy of model parameters and internal state estimation and eliminates interference from spontaneous activity.

The embodiment of the invention provides an object monitoring system and method and electronic equipment, and belongs to the technical field of data monitoring and the technical field of data analysis. The method comprises the following steps: acquiring target multi-modal data of a target object through a multi-modal data acquisition module, and transmitting the target multi-modal data to a data processing module; then, performing motion behavior analysis on the target object through a data processing module to obtain motion behavior analysis data; and carrying out physiological state change analysis on the target object to obtain physiological state change analysis data. Finally, the data processing module transmits the exercise behavior analysis data and the physiological state change analysis data to a display module, and the exercise behavior analysis data and the physiological state change analysis data are displayed through the display module. Therefore, the object monitoring system provided by the invention can collect and analyze the target multi-modal data of the target object, the monitoring comprehensiveness of the object monitoring system is improved, and the time cost of data collection and analysis is reduced.

The present invention patent provides a deep three-dimensional convolutional network detection method for human fall events during exercise. It is characterized in that: (1) every 3 seconds, the next 6-second video segment is monitored, and 18 video images are extracted at equal intervals in the 6-second video segment; the 18 images are input into a specific deep three-dimensional convolutional neural network siamese network, and the determination probability of whether someone has fallen is obtained through this network; the video segment with a probability greater than 0.5 is determined to have a fall event occurring during this time segment.

The invention discloses a lung cancer patient home exercise rehabilitation training system based on a severe game, and the system comprises a patient terminal device which is used for operating a severe game application and collecting exercise behavior data and physiological signals; the medical care terminal management interface is used for medical care personnel to log in and check patient information and training execution conditions; and the server / cloud platform comprises an exercise prescription engine module which is used for generating an individualized exercise prescription based on the baseline evaluation data of the patient. Through a virtual game scene and a hierarchical breakthrough and reward mechanism, the originally boring respiratory training, resistance training and walking training are integrated into a severe game with the sense of sexuality and goals, the participation interest and the sense of achievement of a patient are stimulated, and the possibility of long-term persistence is remarkably improved.

The application provides a processing module, device and system for glucose concentration before and after exercise, the processing module is used for detecting glucose concentration of a subject and giving guidance information, the processing module is configured to determine a fluctuation type of a glucose concentration curve between before exercise and after exercise based on glucose data including time-varying glucose concentration between before exercise and after exercise of the subject, the fluctuation type reflects a change trend of the glucose concentration between before exercise and after exercise, and guidance information or suggestions related to exercise behavior are generated according to the fluctuation type. Therefore, the time spent on seeking doctor's advice can be reduced.

The present invention relates to the field of data communication technology, and particularly to a method and system for monitoring health data in a sports program of a mover, including: obtaining the completion of a sports behavior during use; using sensor technology to synchronously locate and detect the fault data of the sports equipment during use, and analyzing the fault state of the sports equipment during use within a cycle based on the positioning result to obtain the synchronous performance data of the sports equipment during use; for the sports equipment with sequentially fixed actions during use, obtaining the big data of the movement actions of the sports equipment during historical use, generating a general action line, collecting the sports behavior during the use of the sports scenario during use, and completing high-frequency prediction and alert; identifying the potential high-frequency processing behavior of the sports equipment during use based on the sensor data of the sports equipment and the movement actions. The present invention has real-time processing capabilities and can accurately and real-time measure movements and adapt to complex environmental changes.

The invention is directed to compositions and methods for treating or reducing the likelihood of the development of epilepsy in an individual. The method comprises administering to the central nervous system of an individual in need of such treatment a therapeutically effective amount of an agent capable of increasing the expression and / or activity of miR-128.

The present invention discloses a method for classifying sports groups and identifying behavioral patterns based on trajectory semantic similarity. Sports group classification method: Step 1, acquiring cycling motion data to obtain cycling trajectory data; Step 2, performing road network matching to obtain the road type of each cycling trajectory point; dividing each cycling trajectory according to the road type to obtain multiple cycling trajectory segments; determining behavioral attributes and motion attributes, performing attribute calculations and assigning semantics to the cycling trajectory segments to obtain semantic trajectory segments; Step 3, calculating the semantic trajectory similarity to obtain a similarity matrix; Step 4, constructing a similarity network based on the similarity matrix, and using the similarity network to perform community detection to obtain multiple communities, each community being a sports group category. The method of the present invention can effectively classify sports groups and identify complex cycling motion behavior patterns, and provide a scientific basis for urban planning and construction such as greenways, and personalized services and management for different sports groups.

This application provides a high-precision, self-adhesive, stretchable, multimodal pattern system and its construction method, applied to motion behavior monitoring. The method includes the following steps: providing a flexible, stretchable conductive film material; simulating the structural parameters of the motion-sensitive pattern system using simulation software, and based on these parameters, creating a mechanical motion-sensitive pattern using drafting software; importing the mechanical motion-sensitive pattern into a digital printing device, adjusting the device parameters, and printing the pattern onto the flexible, stretchable conductive film material to obtain the digitally printed mechanical motion-sensitive pattern; and conducting a conformity inspection on the digitally printed pattern. Upon passing the inspection, a high-precision, self-adhesive, stretchable mechanical motion-sensitive pattern system is obtained. This application aims to solve scientific motion problems such as posture calibration, rehabilitation training, and movement monitoring in complex motion environments such as sports, fitness, and rehabilitation.

The invention relates to the technical field of artificial intelligence and self-powered wearable sensing, in particular to an intelligent insole based on a self-powered sensor array and a sole pressure behavior recognition method thereof. The system comprises a self-energized sensor array, a signal acquisition and processing module and a data analysis module which are arranged in an insole, the self-energized sensor array is composed of a plurality of friction nanometer power generation units which are arranged in an array mode, and the friction nanometer power generation units are arranged in the forefoot area, the arch area and the heel area of the insole respectively. Electric signals can be generated under the action of plantar pressure or impact without external power supply. The system performs synchronous acquisition, analog-to-digital conversion and normalization processing on the electric signals output by the friction nanometer power generation units to form multi-channel plantar pressure characteristic signals, and realizes reconstruction of plantar pressure distribution and identification analysis of behavior states based on the characteristic signals. The system can effectively reflect stress differences of different areas of the sole, can be used for distinguishing various motion behaviors such as walking and running, realizes self-energy-supply acquisition, visual perception and behavior recognition of sole pressure information, and has good application prospects in the fields of motion monitoring, gait analysis, health assessment and the like.

The invention provides a postoperative rehabilitation standard-reaching evaluation method based on multi-modal motion data fusion, and relates to the technical field of motion behavior analysis and rehabilitation standard-reaching evaluation. Training items and target parameters are set according to operation types and rehabilitation stages, acceleration, angular velocity, displacement and physiological signals are acquired through a wearable acquisition terminal, and a multi-modal data sequence is generated; identifying an effective action section based on the amplitude and period characteristics and forming an execution record; comparing the execution record with a rehabilitation prescription target in the evaluation time window, and calculating a standard score and a comprehensive standard index; and according to the standard index, generating a rehabilitation prompt and dynamically adjusting the training target, thereby realizing quantitative evaluation and individualized optimization of postoperative rehabilitation training.

The invention relates to the technical field of health monitoring, in particular to a child health monitoring data processing method and system.The movement behavior of the current heart rate of a child is obtained by analyzing the difference between the current body temperature data and historical body temperature data and the difference between the current heart rate data and historical heart rate data; judging whether the current body temperature data of the child is normal; under the condition that the body temperature data is judged to be normal, analyzing variation amplitude anomaly and anomaly duration of the current body temperature data, analyzing regional influence of environmental factors in combination with the current position data of the multiple children, obtaining environmental anomaly of the current body temperature data, and further obtaining comprehensive anomaly of the current body temperature data of the children; according to the invention, by eliminating the influence of motion behaviors and environmental factors on the monitored body temperature data, the optimized time interval between the transmission time of the current body temperature data of the child and the transmission time of the next body temperature data of the child is determined, so that more optimized transmission of the body temperature data of the child is realized.

The invention discloses a class of euphorbia kanane type triterpenoids as well as a preparation method and application thereof. The euphorbia kanane type triterpenoids are extracted and separated from Chinese quassia tree, four of the euphorbia kanane type triterpenoids are new compounds, and the rest of the euphorbia kanane type triterpenoids are known compounds. The applicant finds that most of the compounds show remarkable anti-neuroinflammation activity through activity screening. In an MPTP-induced PD mouse in-vivo model, part of the compounds can significantly improve the impaired movement behavior of PD mice; in addition, MPTP-induced brain tissue inflammatory response can be effectively inhibited, and a protection effect on dopaminergic neurons is achieved.

The invention discloses a coronary heart disease patient movement fear exposure training method based on virtual reality, and the method comprises the steps: collecting and preprocessing the multi-modal data of a patient, and generating a standardized input sample set; inputting the standardized input sample set into a deep belief network model to form a multi-dimensional state embedding vector; carrying out global optimization on the deep belief network model by adopting a Peraupis optimization algorithm; the method is applied to multi-modal data, and the exercise fear level, the exercise fatigue degree and the psychological load level are output. A virtual reality training scene, a training item type and training intensity and rhythm are dynamically adjusted, physiological signals and motion behavior characteristics of a patient are monitored, and when indexes exceeding a safety threshold are detected, training is automatically suspended and an alarm is given; after each training is finished, an individualized rehabilitation evaluation report is generated. According to the invention, a deep belief network and a Panka optimization algorithm are fused, and intelligent dynamic regulation and control of virtual reality myocardial infarction rehabilitation training are realized.

The application discloses a kind of intelligent motion state evaluation method and system, belong to intelligent monitoring technical field, comprising: according to disease type and disease grade generates motion scheme, based on disease type and disease grade corresponding motion scheme group disease motion scheme database;Action data and motion state data of user are obtained using sensor;Based on motion state data and the motion scheme in disease motion scheme database analysis user's motion posture, when motion posture is wrong, carry out rectification prompt;Based on motion state data determines the motion of user, and generates motion prompt based on motion situation.The application can realize the real-time accurate feedback of user's performance, help user to correct wrong movement behavior in time, so as to improve the individualization and effectiveness of motion rehabilitation.

A training system may allow for intelligent training of an athlete by utilizing an array of sensors positioned proximal an athlete and allow for the generation of a feedback strategy. The feedback strategy may select at least one manner of communicating training information to the athlete from a plurality of different available manners of communicating training information. An athletic potential of the athlete may be computed in response to athletic behavior of the athlete detected by the array of sensors. The potential module can further estimate a current skill level of the athlete in response to the sensed athletic behavior of the athlete. A difference between the athletic potential of the athlete and the current skill level may be determined the computing device, which allows for the intelligent assignment of training drills selected to increase a proficiency in at least one athletic task associated with the current skill level.

The invention provides a behavioral analysis method based on an echelette grating principle, which comprises the following steps: putting a cage with an experimental animal on an activity monitoring device, arranging a sensor assembly in the cage, and arranging an echelette grating assembly on the activity monitoring device; the weight of a trough in the cage is measured, and whether the experimental animal has a feeding behavior or not is analyzed; measuring the weight of a water tank in the cage, and analyzing whether the experimental animal has a water drinking behavior or not; measuring the movement of the experimental animal, and analyzing whether the experimental animal has a sleep behavior; measuring the movement of the experimental animal, and analyzing whether the experimental animal has a rest behavior or not according to the measured movement data; and measuring the movement of the experimental animal, and analyzing whether the experimental animal has a movement behavior or not according to the measured movement data. Through cooperation of the echelette grating assembly and the sensor assembly, monitoring of five core behaviors of eating, water drinking, sleep, rest and exercise is achieved at the same time, and the one-sidedness of a single index is avoided.

The invention provides an intelligent health interactive physical exercise service system, which relates to the technical field of intelligent health management, and comprises S1, a multi-dimensional data acquisition module, S2, a real-time monitoring and dynamic adjustment module, S3, a real-time monitoring and dynamic adjustment module and S4, a physical exercise service module. The exercise scheme adjusting module is used for dynamically adjusting an exercise scheme of a user according to the multi-dimensional exercise data and the physiological data which are collected in real time, and the individual physique analysis module is used for establishing an individual physique analysis model according to physique indexes, including BM I, basal metabolic rate and past health medical history, of the user. The intelligent health interactive physical exercise service system provides highly personalized exercise suggestions for different users by introducing learning and analysis of historical exercise behavior modes of the users, and can perform dynamic adjustment according to health states, exercise intensity bearing capacities and fatigue thresholds of the users, thereby effectively improving the exercise effect and improving the user experience. Excessive exercise or insufficient exercise is avoided, and exercise safety of the user is guaranteed.

The invention relates to the field of exercise data monitoring and analysis, in particular to an exercise training data monitoring method and device based on the Internet of Things. The method comprises the following steps: deploying a multi-modal sensor array to collect use data of sports equipment in a public ice and snow place in real time, performing data analysis, and generating equipment dynamic use information of different scenes; using intensity quantification is carried out according to the equipment dynamic using information, and a using intensity time sequence curve is generated; periodic fatigue prediction is carried out based on the use intensity time sequence curve, and state fatigue prediction values of all the devices are generated; according to the multi-modal sensor array, carrying out scene exercise people flow statistics to obtain an exercise behavior evaluation report; and carrying out equipment maintenance and equipment resource configuration decision based on the motion behavior evaluation report and the state fatigue prediction value of each equipment. The service life of equipment in public ice and snow places is prolonged, and the user satisfaction degree and safety are improved.

The invention relates to the field of cognition analysis, in particular to a cognition and emotion analysis method and device, electronic equipment and a medium, a first preset action associated with a specific cognition function is triggered by outputting an action indication instruction, and action information (used for representing a feedback action of the target object in response to the instruction) of the target object is obtained in real time; observable motion behaviors can be directly mapped into quantitative cognition and emotion indexes, specifically, a cognition score obtained through calculation based on motion information reflects the response speed of a target object to a motion execution instruction and the consistency degree (namely, the cognition function state) between a feedback motion of the target object and a first preset motion; and the emotion score represents the emotional state of the target object based on action characteristics (such as rhythm, variability and coordination).

The invention is directed to compositions and methods for treating or reducing the likelihood of the development of epilepsy in an individual. The method comprises administering to the central nervous system of an individual in need of such treatment a therapeutically effective amount of an agent capable of increasing the expression and / or activity of miR-128.

A periodic behavior report generation method, apparatus, and system, a storage medium, and an electronic device. The method includes: obtaining behavior data and a health index parameter of a user in a predetermined period, the behavior data including at least one of an exercise behavior, a dietary behavior, or a drug administration behavior; generating, according to the behavior data and the health index parameter, a periodic behavior report in the predetermined period for the user by at least partially using a machine learning model, the periodic behavior report including at least an evaluation result in the predetermined period, a behavior suggestion of a next period, and / or an index target of a next period; updating, based on the periodic behavior report, a behavior label of the user, and obtaining a patient education content matching with the behavior label; and sending the periodic behavior report and the patient education content to the user.

The present invention discloses a training analysis method and system for animal cognitive movement, specifically relating to the field of training analysis technology. The method collects original movement behavior data generated by animals during the cognitive task transition process and performs feature segmentation to generate multiple movement behavior subsequences; in combination with the time nodes of changes in cognitive task types, the task labels of the movement behavior subsequences are extracted, the behavioral feature differences between adjacent cognitive task subsequences are analyzed, and a cross-task difference feature matrix is ​​constructed; based on the reverse cognitive transfer interference index, a cognitive transfer interference identification mechanism is established to identify abnormal behavior fragments and key task transition nodes; in combination with historical cognitive training data, a multidimensional interference evaluation model is constructed to evaluate the cognitive transfer interference degree of the animal in the current training stage; the present invention adjusts the training task sequence according to the matching result of the cognitive transfer interference degree and a preset interference threshold, thereby achieving real-time adaptation and interference suppression of the animal's cognitive transfer state.