15 results about "Movement quality" patented technology

The invention relates to the technical field of intelligent fitness equipment and exercise training control, and provides a training plan dynamic generation and action quality scoring method and system for an intelligent dumbbell. The method comprises the following steps: acquiring action data of a user during training and personalized parameters input by the user in real time through an acceleration sensor, an angular velocity sensor and a pressure sensor which are arranged in the intelligent dumbbell; inputting the action data and the personalized parameters into a preset training plan generation algorithm module so as to generate a stage training plan comprising the action type, each group of training duration, intermittent time and a weight adjustment strategy; an action quality scoring model is constructed, a standard action template is pre-stored in the scoring model, and the standard action template comprises standard trajectory curves, speed threshold ranges, amplitude reference values and force exerting symmetry parameters corresponding to different training actions; and performing multi-dimensional matching analysis on the action data and a standard action template, and dynamically correcting the score in combination with the current fatigue parameter of the user.

The present application discloses an evaluation method and device for strength training, which relates to the field of fitness. A user performs strength training, and the standard movement amplitude of the user performing this action is obtained; the training movement amplitude of the user performing the same action is obtained; the training movement amplitude and the standard movement amplitude corresponding to the same action are compared to obtain a determination result of the movement quality. The present application can cover all dimensions of the user's movement, and realizes a detailed evaluation of the movement quality, with a wider scope of application, higher accuracy, and is convenient for long-term use.

This application relates to the field of intelligent fitness equipment and sports training control technology, and provides a method and system for dynamically generating training plans and scoring movement quality using intelligent dumbbells. The method utilizes the accelerometer, angular velocity sensor, and pressure sensor built into the intelligent dumbbell to collect real-time movement data and personalized parameters input by the user during training. The movement data and personalized parameters are input into a preset training plan generation algorithm module to generate a phased training plan that includes movement type, training duration for each set, rest intervals, and weight adjustment strategies. A movement quality scoring model is constructed, which pre-stores standard movement templates. These templates include standard trajectory curves, speed threshold ranges, amplitude baselines, and force symmetry parameters corresponding to different training movements. The movement data is then matched and analyzed against the standard movement templates in multiple dimensions, and the score is dynamically adjusted based on the user's current fatigue level.

The invention relates to the technical field of medicine and rehabilitation, and discloses an automatic recognition-based whole body exercise quality evaluation and suggestion generation system, which comprises a data acquisition module, a data processing module, an exercise recognition module, an evaluation analysis module, a suggestion generation module and a user interaction module which are in communication connection in sequence. According to the method, data are collected through a camera, human body key point labeling and association are performed in combination with growth data, motion features are extracted by applying a growth association learning algorithm, a motion quality score is obtained based on a GMS standard evaluation method and growth data analysis, personalized rehabilitation suggestions are generated based on a big data expert knowledge base, and the personalized rehabilitation suggestions are displayed through a graphical interface. The defects that a traditional manual evaluation mode consumes manpower and time and is high in subjectivity are effectively overcome, the efficiency, accuracy and individuation degree of whole-body exercise quality evaluation and suggestion generation are improved, and the urgent requirement for exercise function evaluation in the field of medical treatment and rehabilitation is met.

This invention discloses a real-time assessment and correction method for rehabilitation training movements based on deep learning, belonging to the interdisciplinary fields of rehabilitation medicine and computer vision. The method achieves real-time monitoring and intelligent guidance of rehabilitation training movements through the coordinated operation of a human skeletal key point detection module, a movement feature extraction module, a movement quality assessment module, a real-time correction feedback module, and an incremental learning module. It employs a combination of comparative learning and multi-task learning to quantitatively score the quality of movement completion and identify error types; provides real-time correction guidance through augmented reality or voice; and incrementally updates the model based on user feedback data. This invention effectively solves the problems of traditional rehabilitation training relying on manual guidance, high subjectivity, and difficulty in quantification, significantly improving the scientific nature and safety of home-based rehabilitation training.

The invention discloses a campus sports digital management method and system, and relates to the technical field of campus sports intelligent digital management, and the method comprises the steps: firstly collecting student basic sports information, target skill action categories and teaching standard text data, and building a standard action data set; video images, electromyographic signals and skin stretching data in the action execution process of the student are collected, and an action perception set is constructed; calculating an action completion consistency index, comparing the index with a first threshold value, and evaluating the action quality; calculating a muscle-motion cooperation coefficient, comparing the muscle-motion cooperation coefficient with a second threshold value, and monitoring a motion force application structure; based on the trained AI scoring model, automatically outputting an action score and a part error, further calculating a skill growth index, comparing the skill growth index with a third threshold value, and dynamically evaluating the skill growth trend of the student; the system automatically generates a personalized training strategy and a comprehensive scoring report according to an evaluation result, and realizes accurate monitoring and guidance of action quality, power generation coordination and skill growth.

The present disclosure provides systems and methods for generating personalized physical therapy and exercise guidance using artificial intelligence. An Al recommendation engine analyzes user-specific physical condition parameters including range of motion measurements, strength assessments, and functional mobility scores against normative data to identify physical limitations. Based on this analysis, the Al engine generates customized care plans with exercises featuring specific movement parameters and tolerance thresholds tailored to address the identified limitations. The system leverages standard computing device cameras to capture movement data during exercise performance, providing real-time feedback through visual movement guides. This movement data, comprising time-sequenced joint position coordinates, serves as quantitative feedback to continuously improve the Al recommendation engine's effectiveness. The system creates a closed feedback loop where exercise compliance and movement quality measurements are used to refine future care plans, enabling increasingly personalized rehabilitation and fitness experiences without requiring specialized equipment.

The present invention discloses a campus sports digital management method and system thereof, which relates to the technical field of campus sports intelligent digital management. The method first collects students' basic sports information, target skill movement categories and teaching standard text data to establish a standard movement data set; collects video images, electromyographic signals and skin stretching data during the students' movement execution to construct a movement perception set; evaluates the movement quality by calculating the movement completion consistency index and comparing it with a first threshold; calculates the muscle coordination coefficient and compares it with a second threshold to monitor the movement force structure; based on the trained AI scoring model, automatically outputs the movement score and position error, further calculates the skill growth index and compares it with a third threshold to dynamically evaluate the student's skill growth trend; the system automatically generates personalized training strategies and comprehensive scoring reports based on the evaluation results, to achieve accurate monitoring and guidance of movement quality, force coordination and skill growth.

The present disclosure provides systems and methods for generating personalized physical therapy and exercise guidance using artificial intelligence. An AI recommendation engine analyzes user-specific physical condition parameters including range of motion measurements, strength assessments, and functional mobility scores against normative data to identify physical limitations. Based on this analysis, the AI engine generates customized care plans with exercises featuring specific movement parameters and tolerance thresholds tailored to address the identified limitations. The system leverages standard computing device cameras to capture movement data during exercise performance, providing real-time feedback through visual movement guides. This movement data, comprising time-sequenced joint position coordinates, serves as quantitative feedback to continuously improve the AI recommendation engine's effectiveness. The system creates a closed feedback loop where exercise compliance and movement quality measurements are used to refine future care plans, enabling increasingly personalized rehabilitation and fitness experiences without requiring specialized equipment.

The present invention relates to the technical field of physical movement quality assessment, and specifically, to a method for assessing the quality of physical event movements based on marker distribution learning. It at least includes obtaining physical event videos and evaluation images in the original database; clipping the competition videos to obtain multiple main key frame images of limb movements; comparing and evaluating the multiple main key frame images with the evaluation images inside the original database to obtain the evaluation information on the standard degree of the main key frame image movements; obtaining other competition videos in the original database and generating the evaluation information on the standard degree of the secondary key frame image movements; comparing the evaluation information on the standard degree of the main key frame image movements with the evaluation information on the standard degree of the secondary key frame image movements; predicting the standard degree of the personnel's physical movements. By making an auxiliary predictive assessment of the standard degree of the physical movements of the subsequent personnel during a physical event with the same movements as the main key frame images, the present system can have a preliminary understanding of the current physical event.

The title of the invention is 'Portable Electronic Step Board with the Ability to Recognize the Quality of Various Foot Movements.' The technical field of this invention relates to electrical and electronic engineering, utilizing a circuit with various electronic components. Overall, the application of this system, due to its smartness and ability to count different foot movements, spans universities, schools, sports centers, gyms, homes, and more. The system has been designed based on electronics and the functionality of an electronic circuit, utilizing data from pressure, force, weight applied, and the number of pressures exerted on the step board. Evaluating movement quality, distinguishing between correct and incorrect movements, and counting accurate foot movements in home or gym aerobic exercises, as well as sports competition judging, are challenging tasks requiring high precision. To address this issue, the designed system, based on data from foot impacts on the step board, provides precise output to the user, including the quality of movements, differentiation between correct and incorrect movements, the count of correct single and double foot movements, and time.

This invention discloses an interactive sports training assessment method and system, belonging to the field of sports training assessment technology. The invention utilizes a data acquisition module to collect athletes' physical condition data, stride length data, stride frequency data, and center of gravity shift data in real time. Then, a data processing module sequentially calculates and outputs a stride length score (BH), an overall movement quality score (ZT), and a new ideal stride length (LB). new Using the results feedback and adjustment module, the new ideal stride length LB is adjusted. new The results are compared with the ideal stride length (LB) obtained from the stride length data, and the correction is evaluated. The new ideal stride length (LB) is then replaced with the new ideal stride length (LB) in the next training session. new This invention enables athletes to make targeted improvements, and reasonable stride length, coordinated stride frequency and stable body center control help improve running efficiency. The ideal stride length (LB) is optimized in real time based on the overall running motion quality, so that training can be flexibly interactive and adjusted according to the athlete's real-time performance, thereby continuously improving training effect and athletic performance.

The invention discloses an interactive physical training evaluation method and system, and belongs to the technical field of physical training evaluation.A data acquisition module is used for acquiring physical condition data, stride data, stride frequency data and gravity center shift data of athletes in real time, and then a data processing module is used for processing the data; and sequentially calculating and outputting a stride score BH, an overall action quality score ZT and a new ideal stride LBnew, performing result comparison and correction evaluation on the new ideal stride LBnew and an ideal stride LB of a stride data result by using a result feedback and adjustment module, and replacing the ideal stride LB with the new ideal stride LBnew during next training, so as to obtain a new ideal stride. According to the method, the athletes can be improved in a targeted mode, reasonable stride, coordinated stride frequency and stable body gravity center control are beneficial for improving running efficiency, the ideal stride LB is optimized in real time according to the overall running action quality, flexible interaction and adjustment can be conducted on training according to real-time performance of the athletes, and the training efficiency is improved. Therefore, the training effect and the exercise performance are continuously improved.

The application discloses a human body coordination movement quality evaluation training device and method, and relates to the technical field of sports training and evaluation devices. The device comprises an intelligent terminal, a plurality of variable color grooves and a plurality of variable color balls. The intelligent terminal is used for controlling each variable color ball to change color and controlling each variable color groove to change color. When a user throws a variable color ball, it is determined whether the current color of the variable color ball thrown by the user is consistent with the current color of the variable color groove to be thrown. A judgment result is obtained, and the time length between the starting time when the user is allowed to throw and the time when the variable color ball thrown by the user is thrown into the variable color groove to be thrown is obtained. Until a plurality of judgment results and the time length corresponding to each judgment result are obtained, the evaluation result of the human body coordination movement quality of the user is obtained. The application is suitable for human body coordination movement quality evaluation training of people of all ages, especially for adult and elderly people who need rehabilitation training.

This invention provides a rehabilitation training movement assessment method based on artificial intelligence and the Internet of Things, belonging to the field of movement assessment technology. The method includes: synchronously collecting multimodal raw data streams of users performing rehabilitation training movements through an IoT sensor unit cluster; performing spatiotemporal alignment, filtering and denoising, and feature decomposition on the data to obtain standardized multidimensional movement feature time-series data; extracting quantitative assessment parameters in real time based on a rehabilitation medicine knowledge model; inputting the parameters into a pre-trained movement quality assessment and compensation pattern recognition model to generate intelligent assessment results containing specific compensation pattern labels and root cause inferences; fusing the intelligent results with historical data to generate a visualized assessment report; and dynamically adjusting the parameters of the personalized rehabilitation prescription for the next cycle based on trend analysis. This invention achieves automated, quantitative, and real-time assessment of the intrinsic quality, hidden compensation, and kinetic chain synergy of training movements without on-site therapist supervision.