7 results about "Movement quality" patented technology

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 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 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 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 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.