238 results about "Movement analysis" patented technology

Systems and methods for four-dimensional analysis of a patient's coronary arteries and myocardial wall. An example method includes accessing angiographic x-ray images of one or more arteries, with the x-ray images depicting the arteries from respective angles, with each x-ray image being associated with a cardiac phase of cardiac phases, and with the cardiac phases being included in a cardiac cycle. A three-dimensional model of the arteries is generated for each cardiac phase based on a subset of the x-ray images which are associated with the cardiac phase. A four-dimensional representation of the arteries is generated throughout the cardiac cycle based on the three-dimensional models for the cardiac phase. Information associated with the four-dimensional presentation is presented.

The invention relates to the field of intelligent parking scheduling, in particular to a big data analysis method and system for intelligent parking. The method comprises the following steps: obtaining all parking lot information in a city based on a parking management platform, and marking parking lot nodes; performing three-dimensional distribution visualization on parking lot nodes based on all parking lot information in a city, and constructing a parking lot three-dimensional distribution visualization network; acquiring urban road traffic flow data based on a multi-source sensor; real-time vehicle dynamic movement analysis is carried out on the urban road traffic flow data, multi-time-point distribution evolution is carried out, and an urban digital traffic track portrait is constructed; obtaining a historical city parking operation log; and carrying out parking demand trend evolution on the historical city parking operation log, and then carrying out dynamic parking demand prediction in a future period, thereby obtaining dynamic parking demand prediction data. According to the invention, efficient and high-utilization-rate urban parking scheduling is realized.

The invention belongs to the field of aquaculture, and particularly relates to an underwater fish multi-target identification, tracking and motion analysis method, the method is based on a multi-target attitude tracking framework based on direction perception, and the framework comprises an improved YOLOv11 model (YOT model) and a directional bounding box attitude tracking module (OPBT module). The YOT model integrates directional bounding box detection and attitude estimation, adopts a task-aligned dynamic detection head structure, and combines a multi-task loss function to realize efficient direction perception detection and key point positioning. The OPBT module is based on an unscented Kalman filter, integrates rotation IoU, angle similarity and body length similarity, and improves identity continuity and occlusion recovery capability in multi-target tracking. The framework is suitable for complex scenes such as high-density fish schools, the real-time processing performance can be kept, meanwhile, the detection and tracking precision can be remarkably improved, and stable and reliable technical support is provided for individual behavior analysis.

The systems and methods described herein can identify meaningful relationships between variables, such as particular investments or general asset classes. Unlike conventional correlation analysis, these systems and methods provide an improved technique of co-movement analysis that implements a threshold to eliminate data “noise” and then discretizes the remaining observations to normalize any outliers. Such co-movement analysis has numerous advantages over known techniques for characterizing relationships between variables.

The invention discloses a wearable knee joint self-powered sensing system and a use method, relates to the technical field of motion monitoring, and solves the technical problem that a motion detection sensor integrating light weight and a self-powered mode is lacked in the prior art. The motor comprises a stator part and a rotor part which are rotationally arranged, a center shaft penetrating through the stator part is arranged on the rotor part, and the stator part and the rotor part are rotationally connected through the center shaft; and a motion state monitoring module and a self-powered module are sequentially and coaxially arranged at the connecting part of the stator part and the rotor part, so that the rotation angle and direction of the knee joint are accurately detected, and key data support is provided for subsequent motion analysis and health monitoring.

The invention discloses an artificial intelligence-based postoperative rehabilitation auxiliary monitoring system for orthopedic surgery. The system comprises a data acquisition module, a rehabilitation progress evaluation module, a motion analysis module, a risk early warning module, an optimization system module and a personalized rehabilitation module. The invention belongs to the technical field of rehabilitation monitoring, and particularly relates to an orthopedic surgery postoperative rehabilitation auxiliary monitoring system based on artificial intelligence. According to the scheme, motion analysis is adopted, postures are decomposed into a standard state and an error state, modeling errors are considered, a posture updating equation is calculated to reflect actual posture changes, and the actual posture changes are compared with the updated standard state; estimating and correcting the error state in real time, and adjusting a rehabilitation treatment scheme; system optimization is adopted, attention mechanism processing is carried out, data features are extracted, training efficiency is improved, positive model parameters are updated by minimizing prediction errors, a reverse model is optimized, a curiosity loss function is constructed, curiosity-driven exploration is achieved, internal rewards are introduced, and the rehabilitation effect and efficiency are improved.

A method for analyzing a ball game motion includes: in response to entering a serving stage, obtaining a motion trajectory of a ball by fitting based on a ball game video acquired; determining a hitting coordinate of a motion subject, a hitting posture of the motion subject, and a landing coordinate of the ball based on the motion trajectory and the ball game video; and obtaining a motion analysis result based on the hitting coordinate, the hitting posture, and the landing coordinate, to display the motion analysis result in real time through a display device.

This invention can acquire a difference in motion of a motion acquisition target object without detecting the difference from the outside of the motion acquisition target object. A motion acquisition device includes two acceleration sensors, one angular velocity sensor, an information acquisition unit, and a motion analysis unit. The two acceleration sensors and the one angular velocity sensor are arranged in the motion acquisition target object rotated around a rotation axis. The information acquisition unit acquires acceleration information detected by the two acceleration sensors and angular velocity information detected by the one angular velocity sensor. Based on the acceleration information and the angular velocity information acquired by the information acquisition unit, the motion analysis unit estimates a distance from the motion acquisition target object to the rotation axis and an attitude angle of the motion acquisition target object.

The invention discloses a non-contact charging pile interaction method and device and a medium, belongs to the technical field of charging piles, and aims to solve the technical problems that non-contact interaction of existing charging piles has certain limitation, user intentions are difficult to understand, and the accuracy of action recognition is low. The method comprises the following steps: performing preliminary wake-up control on a charging pile system to obtain a wake-up control strategy; performing dual verification of the optimal interaction area and the gesture action on the user to obtain an operable control strategy; performing motion analysis on the wrist action of the user, and generating a charging adjustment control strategy based on a charging power value identified by the wrist action; performing security payment control under two-factor authentication on the gesture action of the user to obtain a security payment control strategy; and based on a wake-up control strategy, an operable control strategy, a charging adjustment control strategy, a safety payment control strategy and an environment self-adaptive control strategy, non-contact charging control of the user on the charging pile system is completed.

A machine-learning (ML) based motion analysis and training method and systems are provided. The method includes acquiring data of measurements of kinematics of a user performing a task with his / her hands or arms; analyzing the data of measurements based on a machine-learning (ML) model to evaluate patterns of hand / arm movements of the user; and providing feedback and / or advice based on results of the analysis for improvement of skills of the hand / arm motions of the user. The analyzing the data of measurements includes data preprocessing for preparing raw data for analysis, feature engineering for creating relevant features from the raw data for training the ML model, model training for training the ML model based on the preprocessed data, model evaluating for assessing performance of the trained model based on predetermined evaluation metrics, hyperparameter tuning for fine-tuning the hyperparameters of the ML model to improve its performance, model validating, and model testing.

The invention relates to the technical field of action analysis, in particular to a multi-angle dance video analysis system which comprises an action capture feature extraction module, an action data multi-scale analysis module, an action analysis and mapping module and a style classification and prediction module. According to the method, through a multi-angle video recording and Vietories-Rips complex method, the space connection mode of dance is effectively identified and extracted, the geometric structure of the action is captured, three-dimensional action data analysis and multi-scale persistent homology analysis are provided to enhance analysis on the time sequence and the space layout of the action, and the dance style and the action mode are accurately distinguished; high sensitivity to fine difference of styles is brought, dance style evolution can be accurately predicted, action data is mapped to a low-dimensional space through multi-dimensional scale analysis, data visualization and analysis capacity is remarkably improved, action similarity and difference are displayed, dance education, choreography and therapy are assisted, full-process optimization of data analysis is achieved, and the method is suitable for popularization and application. And a comprehensive and deep dance motion analysis tool is provided.

The invention relates to the technical field of intelligent medical rehabilitation and computer vision, in particular to a muscle-bone rehabilitation posture motion analysis system based on visual recognition. Comprising a visual analysis management center, a motion feature extraction unit, a dynamics inversion unit, a compensation coupling analysis unit, a time sequence conduction unit and a rehabilitation evaluation decision unit. Geometric compliance is analyzed and discriminated through dominant kinematics characteristics; if so, performing visual moment inference consistency analysis by using a dynamic inversion unit; in response to moment discretization, acquiring and analyzing a compensation coupling coefficient and kinematic chain conduction delay respectively; and finally, calculating a compensation evaluation index through multi-dimensional compensation quantitative matching analysis, and generating a recessive compensation or effective driving signal. According to the method, a layered filtering mechanism is constructed, implicit problems are dominated, and the blank of monitoring of the internal force exerting mode in non-contact rehabilitation evaluation is filled up.

The invention provides a coal mine drilling machine drill rod target detection and movement analysis counting method and system and a medium, and the method comprises the steps: collecting an underground drilling video, segmenting frames, and marking the whole drilling machine and a tail boundary frame; a multi-level feature extraction module MCFN is added in front of a YOLO model C2F module, and a detection model is trained and optimized; the whole drilling machine and a tail frame are detected frame by frame, four areas are divided based on the center of the whole frame, the overlapping area of the tail frame and two diagonal areas is calculated, and the high-frequency direction is counted through a sliding window to determine the working direction; dynamically adjusting a forward / backward area ratio according to a tail / overall frame area ratio, and diagonally dividing a dynamic area along a working direction; the tail is initialized to be in a backward state, a forward / backward counter is triggered through the center point position of the tail, and when a certain counter reaches a threshold value, the moving state is switched; when the state is switched from advancing to retreating, the number of the drill rods is plus 1, and the video is circularly processed until the operation is finished. According to the method, multi-stage features and dynamic region division are fused, and accurate counting of the drill rods in the underground complex environment is achieved.

The embodiment of the invention provides a motion analysis method and device, electronic equipment and a storage medium, and belongs to the technical field of machine learning. The method comprises the following steps: acquiring current motion data and historical motion data; training a preset recurrent neural network according to the historical exercise data and the corresponding historical exercise scores, and constructing a target score prediction model; inputting the current motion data into the target score prediction model to obtain a predicted score result; constructing a problem instruction based on the predicted score result, the current exercise data, the historical exercise data and the historical exercise score; and inputting the question instruction into a preset large language model, and outputting an exercise training suggestion report. The exercise data can be comprehensively analyzed based on the target score prediction model and the preset large language model, the exercise training suggestion report is quickly and accurately generated, and the problems that an existing exercise analysis method depends on manual subjective judgment and is low in efficiency and score prediction accuracy are solved.

The present invention relates to the field of image recognition technology, and more specifically to a method, system and storage medium for analyzing aerobics training movements. The method comprises: step S1: obtaining a user's first movement video in real time and monitoring the user's heart rate; step S2: obtaining a second movement video, periodically obtaining sub-movement frames from the second movement video, and extracting a first joint trajectory, comparing it with a reference joint trajectory, obtaining a first comparison result, and obtaining a sub-movement trajectory according to the first comparison result, the second joint trajectory and the third joint trajectory; step S3: comparing the line changes of key points with the reference lines of key points, and obtaining a second comparison result; step S4: estimating the first heart rate change of each target sub-section in the sub-movement trajectory and comparing it with the second heart rate change, and obtaining a third comparison result. The present invention solves the problem of inaccurate analysis results of aerobics training movements and improves the accuracy of the analysis results of aerobics training movements.

The invention relates to a motion function data acquisition method and system based on human-computer interaction. The motion function analysis method is applied to a motion function analysis device and comprises the steps that in response to an initial position instruction of an abnormal object, initial motion analysis is conducted based on historical treatment data of the abnormal object, and initial motion data is presented; when the corresponding initial body image is detected, pose analysis is executed, and a body condition labeling box is displayed to a labeling person. After annotation confirmation is received, carrying out movement analysis in combination with annotation data and historical treatment data, and outputting action change data; and if the current body image corresponding to the current body image is detected, taking the current body image as a new initial image, and repeating the pose analysis and labeling process. And until an annotation stop instruction is received, the system summarizes each annotation and historical treatment data to carry out object function analysis, and motion function data of the abnormal object is generated. By adopting the method, the accuracy of understanding the current abnormal information of the abnormal personnel can be effectively improved.

The invention discloses an abnormal behavior identification and risk management and control method in man-machine cooperation, and belongs to the technical field of robots, and the method comprises the steps: S10, marking a man-machine interaction key part, and creating a special data set for man-machine cooperation; s20, training data by using a YOLOv11-Pose model, and constructing a real-time detection model after optimization; s30, using a kernel correlation filter of a structured SVM to track key parts of the person and the robot in the cooperation area, and recording a motion track; s40, based on the motion trail, establishing a man-machine cooperation motion analysis model; s50, constructing a man-machine cooperation safety risk prediction model in combination with various factors; s60, according to risk prediction, risk grading is carried out on the cooperation scene, and corresponding measures are taken; the method has the beneficial effects that the cooperation scene of the robot and an operator is monitored in real time, accidents caused by close-range contact are prevented, the safety in a man-machine cooperation environment is effectively improved, the accident occurrence probability is reduced, and the continuity and effectiveness of operation are guaranteed.

The invention discloses a motion analysis method and device based on sound feature recognition, a terminal and a medium, and the method comprises the steps: when a user moves, determining a sound feature period based on a sound signal collected in real time by a sound collection module, the sound feature period being used for reflecting a change rule of the sound signal, the sound acquisition module is arranged on one side of the motion auxiliary equipment; based on the sound characteristic period, determining a gait period corresponding to the sound characteristic period, the gait period being used for reflecting a gait rule of user motion; and based on the sound characteristic period and the gait period, determining the motion posture of the user. According to the method, the motion posture of the user can be accurately analyzed, and the analyzed motion posture can be used for controlling motion auxiliary equipment or recording motion habits of the user, so that the motion safety of the user is better guaranteed.

To provide a motion analysis device that can be used for evaluating a motor function or motion disorder.SOLUTION: A motion analysis device (20) includes elbow flexure ratio calculation means (222) for calculating an elbow flexure ratio of an upper limb in elevation exercise which is expressed by an expression (1) or an expression (2) from respective three-dimensional coordinate data of the shoulder, elbow, and hand of a person to be evaluated when the person to be evaluated is in upper limb elevation exercise. In the expression (2), SH represents a vector from the shoulder to the hand, SE represents a vector from the shoulder to the elbow, and EH represents a vector from the elbow to the hand.SELECTED DRAWING: Figure 1

The present invention relates to a system, device, and method for rehabilitation and tele- rehabilitation that leverages artificial intelligence (AI) and motion analysis. The system utilizes one or more standard RGB cameras to capture videos of a user's movements without requiring additional sensors. The AI analyzes the movement of individual limbs and joints, providing real-time feedback to the user. This feedback helps ensure proper movement execution, which is critical in rehabilitation, fitness training, and sports. The system can be used autonomously or in tele-rehabilitation, where a remote user, such as a physician or trainer, or an AI-driven system monitors and adjusts the user's movements. Additionally, the invention incorporates a smart dynamic body-weight support system (sDBWSS) equipped with smart harnesses that allows for variable weight suspension depending on the exercise. It also includes a treadmill controlled by the horizontal movements of the patient that replaces the rail.

The invention provides a regional anesthesia puncture path planning method and system under ultrasonic guidance, and relates to the technical field of ultrasonic image.According to the regional anesthesia puncture path planning method and system, dynamic motion information of a tissue structure is obtained by collecting ultrasonic image data of a target region and conducting motion analysis, and electrocardiosignals are synchronously collected to determine heart beat time phase information; determining a vascular movement mode based on the two, learning a time change rule by using a long-short-term memory network, and establishing an association relationship between a vascular spatial position and a heart beat phase; constructing a motion prediction model to predict the future motion trend of the blood vessel, and fusing the respiratory motion information to correct to obtain the blood vessel motion information; and finally, a safe puncture window is calculated according to the information, a regional anesthesia puncture path is planned, vascular movement prediction and safe puncture window calculation based on vascular movement rule learning and breathing correction in regional anesthesia under ultrasonic guidance can be achieved, and then the puncture path is precisely planned.

The invention discloses an athlete action analysis and training optimization method and system based on big data, and relates to the technical field of exercise training.Multiple array sensors are deployed to collect exercise data, including static data and dynamic data, of a trainee, and meanwhile medical images of the trainee are obtained; constructing a basic model of the trainer based on the motion data, wherein the basic model comprises a body model and a motion model; acquiring athlete big data to construct an athlete big database, and generating a shadow model based on coupling of the athlete big database and the body model; calculating the coincidence degree of the basic model and the shadow model, generating an optimal function unit based on the coincidence degree, and guiding training of other body function units based on the optimal function unit to obtain training data. According to the method, the trainee basic model and the athlete shadow model are constructed, a scientific target reference is set for personalized training, and the pertinence and operability of training optimization are greatly improved through data-driven personalized model construction.

We provide a system that protects personal privacy by preventing infringement of portrait rights and damage caused by being photographed in photos due to unauthorized posting on social media. Solution: Utilizing multiple biometric identifiers, including the user's facial image, 3D body scan, clothing, voice, and movement analysis, posts on social media are constantly monitored. When a reflection is detected, a notification and deletion request is automatically sent. The system is comprised of a composite module that performs monitoring, detection, notification, request, and billing, and detection accuracy is continuously improved using AI. It has a flexible monitoring system according to billing levels and an automatic linking function with social media operators. [Effects] High-precision detection and a sustainable monitoring model will enable the protection of individuals' portrait and privacy rights, as well as the prevention of digital tattoo formation.

The invention relates to the technical field of motion analysis, in particular to an aerobics motion accurate analysis system based on multi-modal data fusion, which comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal data fusion module, a motion evaluation module and a feedback module. Acquiring and processing data to obtain an Euler angle, an action time sequence and an acceleration mean value, and splicing a sensor feature vector and an image feature vector to form a joint feature vector; the relative motion trajectory similarity, the relative angle similarity, the motion rhythm error and the trajectory smoothness are calculated through the combination of the feature vector, the Euler angle, the motion time sequence and the acceleration mean value, the motion standard is evaluated according to the relative motion trajectory similarity and the relative angle similarity, the rhythm coordination is evaluated according to the motion rhythm error, and the motion rhythm error is evaluated according to the motion rhythm error. And evaluating action fluency according to the track smoothness, reminding the user in real time, and generating a training report containing action standard, rhythm coordination and action fluency after the aerobics exercise is finished.

To solve the problem of being difficult for even a person of not being a specialist to grasp feelings and a physical condition of a pet animal.SOLUTION: A system includes movement analysis means by an AI, cry analysis means, and other sensor data analysis means as means for grasping feelings and a physical condition of a pet animal.SELECTED DRAWING: Figure 1

A method for learning a person movement model includes obtaining a data stream having time series of measured multi-dimensional movement data of the person from at least one sensor; segmenting the data into time segments, segment corresponding to one movement step; storing each segment into a first storage section with a predetermined memory size, performing clustering of the segments by computing distances between the segments, learning a two-dimensional non-linear topology preserving embedded feature vector based on the computed distances, clustering the vectors, aligning segments of each cluster and averaging the aligned segments of for generating a prototype segment for each cluster; generating and storing a movement model by comparing the determined number of clusters of a current clustering with the determined number of clusters from the previous clustering stored in a second storage section.

The invention provides a traditional opera action analysis method and device fusing an energy field, equipment and a storage medium. Relates to the field of Chinese opera action analysis and digital protection. According to the method, a time-joint-energy field three-dimensional analysis model is constructed based on three-dimensional attitude, mechanical dynamic and thermal gradient data acquired by multi-modal motion capture. The method focuses on feature extraction and energy analysis at the same time, in the aspect of feature extraction, an adaptive derivative operator is adopted to accurately capture subtle motion changes, and curvature extreme value detection is applied to accurately position motion trajectory inflection points. In an energy analysis level, a diffusion tensor and source item intensity are calculated based on an energy transfer partial differential model. In addition, the energy transfer path and the key force application point in the throwing and recycling actions of the water sleeve are mainly analyzed, and the instantaneous action characteristics of the water sleeve are accurately captured. Finally, a visual analysis report is generated, accurate data support is provided for teaching, digital inheritance and virtual performance, and scientific evaluation and quantitative analysis of opera actions are achieved.

The invention relates to the field of rock mass structure characterization, in particular to a rock mass structure quantitative characterization method based on block shape clustering analysis. The method comprises the following steps: constructing a DFN model based on field fracture data of a real rock mass, and obtaining block system data through a three-dimensional block recognition algorithm; the method comprises the following steps: normalizing the volumes of all blocks, constructing point cloud data for normalized new blocks, aligning point clouds of pairwise polyhedrons by applying a point cloud registration method, calculating the maximum volume overlapping ratio of pairwise polyhedrons, constructing a shape similarity matrix, performing spectral clustering analysis on the shape similarity matrix, constructing a representative shape, and calculating the size distribution of each group of blocks. And constructing a representative block and the like to quantitatively characterize the rock mass structure. Compared with a traditional block shape chart method, the method has the advantages that complex shape features of the block are quantitatively represented through the representative shape, the representative block is further constructed to reflect real shape and size features of a block system, and a basis can be provided for construction of a refined model for motion analysis of rockfall disasters.

The systems and methods described herein can identify meaningful relationships between variables, such as particular investments or general asset classes. Unlike conventional correlation analysis, these systems and methods provide an improved technique of co-movement analysis that implements a threshold to eliminate data “noise” and then discretizes the remaining observations to normalize any outliers. Such co-movement analysis has numerous advantages over known techniques for characterizing relationships between variables.

The invention relates to the technical field of ankle pump motion monitoring, and discloses an ankle pump motion monitoring data processing method and system, and the system comprises a patient data collection module, an ankle pump motion data detection module, a physiological signal collection module, an ankle pump motion analysis module, and a motion data evaluation module. The patient data acquisition module acquires age and physiological data actively uploaded by a patient, and constructs a physiological database. A motion database is constructed by obtaining motion data and interference data of a patient in the ankle pump motion process, meanwhile, physiological signals of the patient in the ankle pump motion process are obtained, and a physiological signal database is constructed; the exercise integrity coefficient and the exercise interference coefficient are combined with the physiological data weight and the physiological signal weight to comprehensively calculate the ankle pump exercise coefficient, and the exercise intensity adjustment suggestion of the patient is given based on the ankle pump exercise coefficient, so that the intensity of the patient during the next training can be effectively determined according to the omnibearing information of the patient in the training process; and a progressive growth type training process is completed.