34 results about "Exercise Type" patented technology

A system includes a processor that is configured to receive input information including a user's training goal and type of sport, generate a training plan based on the received information, capture and upload a training video of the user performing training according to the training plan, analyze the uploaded training video to identify errors in the user's movements and points for correction, and provide the identified correction points and specific methods for improvement to the user.

A server apparatus may be provided. The server apparatus may comprise a communication circuit memory and a processor. The processor may: when an exercise type in which a user participates in a challenge and a target value of the user are received from a user device through a communication circuit, identify a type of information related to the exercise type; in response to having transmitted a request for information corresponding to the identified type to the user device through the communication circuit, when the information corresponding to the identified type is received from the user device, identify an exercise ability of the user for the exercise type on the basis of the received information; acquire a correction value for correcting the target value of the user, on the basis of an exercise ability of each of a plurality of users who have participated in the challenge; transmit information about the correction value to the user device through the communication circuit so that the user device corrects the target value on the basis of the correction value; and display a user interface (UI) screen comprising the corrected target value and a character corresponding to the corrected target value.

The invention relates to a motion breathing rhythm optimization method, system and device based on somatosensory synchronization of smart clothing and a medium, and the method comprises the steps: collecting human motion posture data in real time through a sensor assembly, extracting the main frequency and phase features of a motion rhythm through a time-frequency mixed analysis method, and recognizing the type and intensity of a current motion; according to the current motion type and intensity, calling a matched inspiration and expiration ratio parameter in a preset respiration database, predicting a starting point of a next period by combining the phase characteristics, and generating a target respiration rhythm template containing a phase compensation amount; generating a pneumatic tactile waveform parameter group based on the target respiratory rhythm template, controlling the working time sequence and the pressure gradient of a flexible pneumatic assembly of the intelligent garment according to the pneumatic tactile waveform parameter group, and forming simulated respiratory fluctuating waves propagating along the direction of the intercostal muscles on the body surface of the human body. Therefore, implicit accurate regulation and control of the motion respiratory rhythm are achieved through the synergistic effect of pneumatic tactile feedback and an intelligent algorithm.

The utility model discloses a sole with adjustable rigidity, which comprises an outsole and a midsole which are sequentially arranged from top to bottom, the outsole and the midsole are bonded together, the upper end of the midsole is provided with an embedding groove, an insole is embedded in the embedding groove, the bottom of the insole is bonded with a rigid support plate, and the rigid support plate is connected with the outsole and the midsole. The insole is provided with an embedded groove, the rigid supporting plate is arranged between the embedded groove and the insole, the Shore hardness C of the insole ranges from 38 degrees to 45 degrees, the thickness of the insole ranges from 10 mm to 30 mm, the thickness of the rigid supporting plate ranges from 0.6 mm to 1.5 mm, the Shore hardness C of the insole ranges from 38 degrees to 45 degrees, and the thickness of the insole ranges from 10 mm to 20 mm. The depth of the embedded groove is equal to the sum of the thickness of the insole and the thickness of the rigid supporting plate, and the shape and the size of the embedded groove are matched with the shape and the size of the insole respectively. According to the shoe sole with the adjustable rigidity and the shoe, the overall longitudinal rigidity of the shoe sole is adjusted to adapt to different crowds and exercise types, so that the shoe sole has better cushioning performance and exercise performance.

The invention discloses a body moving posture angle video automatic identification method, which belongs to the technical field of artificial intelligence and comprises the following steps of system starting and configuration, data acquisition and motion type identification and posture analysis and angle calculation. According to the method, the key posture angles such as knee joint angles and shoulder angles are automatically analyzed and calculated through video streaming or real-time capture of movement actions of athletes and by means of advanced image processing and deep learning algorithms. Meanwhile, the method can automatically identify the exercise type and provide personalized feedback and suggestions, thereby helping the athlete to optimize the training effect, improving the exercise efficiency and reducing the exercise injury. The method has high innovativeness, and the accuracy, real-time performance and adaptability of an existing motion attitude analysis system can be effectively improved.

The invention discloses a personalized nutrition scheme generation method and system for height increase, and relates to the field of health management, and the method comprises the steps: collecting static biological data, parent height data, health background questionnaire data and intestinal flora samples of a user, calculating the genetic target height and basal metabolic rate of the user according to the static biological data and the parent height data, generating a personal file of the user; extracting height, weight and leg length data of the user from a personal file of the user, obtaining stress distribution of a lower limb epiphyseal plate of the user under different exercise types through biomechanical simulation, and generating a personalized exercise scheme according to a stress distribution result; and estimating daily energy consumption according to the basal metabolic rate and the personalized exercise scheme to obtain the daily total energy demand of the user. According to the method, the problem of insufficient scheme accuracy and adaptability caused by neglecting individual nutrition absorption difference and biomechanical response characteristics in a traditional method is effectively solved.

The utility model discloses a multi-station training device, which relates to the field of fitness equipment, and adopts the technical scheme that the multi-station training device comprises a first supporting unit, a second supporting unit and a third supporting unit, the multiple movement units are located on the four side portions of the first supporting unit correspondingly; each moving unit is correspondingly provided with a moving part, and the moving parts are connected with the first supporting units; the number of the counterweight units is multiple, the counterweight units are arranged corresponding to the motion units, and the counterweight units can be linked with the motion parts in the motion units. The fitness training device has the advantages that the training device can be used by multiple persons at the same time according to requirements, and different users can do different types of fitness exercises. The space utilization rate of the fitness equipment is better improved, and the fitness exercise types provided by the same set of fitness equipment are enriched. The device has good expansibility, and more diversified fitness devices can be combined by adjusting the types of the training units.

Multi-device cooperative AI personalized exercise planning generation system. The present application belongs to the technical field of intelligent fitness and health management, and specifically relates to a multi-device cooperative AI personalized exercise planning generation method, which comprises the following steps: S1: obtaining a user basic parameter set and a current dynamic physiological parameter set; S2: performing preprocessing and feature extraction based on the basic parameter set and the current dynamic physiological parameter set, generating a multi-dimensional derived feature vector and a standardized user vector through a feature engineering module; S3: performing feature fusion using the standardized user vector and the multi-dimensional derived feature vector to generate a multi-dimensional user state portrait feature sequence; and S4: performing parallel analysis and decision on the multi-dimensional user state portrait feature sequence using a multi-source hybrid decision model to generate an exercise type preference vector. The present application can realize highly personalized, scientific and safe dynamic exercise planning which can be continuously executed across devices by fusing multi-source heterogeneous data, performing parallel multi-source hybrid decision, dynamically weighted fusion and exercise science knowledge graph mapping.

The present invention relates to a system designed to collect fitness data during strength training exercises and transmit this data to a receiving computer, such as a laptop or smartphone, via wireless or wired communication. The system comprises a central computing device that can be mounted to various attachment points suitable for different exercise types. Certain attachments include force sensing elements that, when connected to the central computing device, enable the transmission of force readings. These readings, combined with device displacement, allow for the calculation of work performed.

One aspect of the invention provides a computer-based method for providing corrective feedback about exercise form, the method comprising; recording a user performing a specific exercise: evaluating, by the computer, with machine learning, computer vision, or deep learning models that have been previously trained in order to evaluate the form of a user by training on labelled and or unlabeled datasets that consist of: both correct and incorrect exercise form for the different types of exercises being evaluated; identifying the user throughout the video, the exercise type, each repetition of the exercise, the user's errors in form; and then generating, by the computer, corrective feedback for the user on how to improve exercise form for subsequent repetitions; and communicating, via an output device, the corrective feedback to the user.

The invention discloses a motion guiding method and device based on an intelligent ring, the ring and a storage medium, and relates to the technical field of intelligent wearable device.The method comprises the steps that motion data and physiological signal data of a user are collected in real time; identifying a motion type of the user based on the motion data; determining an exercise efficiency index of the user according to the exercise type and the exercise data, and determining an exercise load level of the user according to the exercise efficiency index and the physiological signal data; according to the exercise efficiency index and the exercise load level, a tactile guidance signal is output, and the tactile guidance signal comprises a rhythm guidance signal and / or an action correction signal. The exercise effect of the user during exercise can be optimized through the intelligent wearable device.

This GUI is an image for selecting motion cards (information display frames) for various types of exercise, such as cycling, and detects data related to the exercise in real time and displays it on the display of the electronic device.When the user touches the central section of the "Image Diagram," the "Image Diagram" changes in the order "Image Diagram 1 Showing the Changed State" to "Image Diagram 2 Showing the Changed State."

An electronic device according to an embodiment of the present disclosure comprises at least one sensor, a memory storing instructions, and at least one processor. The instructions, when executed by the at least one processor, may cause the electronic device to: acquire a biometric signal of a user by using the at least one sensor; recognize at least one of an exercise state or an exercise type of the user on the basis of sensor data acquired by using the at least one sensor; determine a weight to be applied to biometric data corresponding to the biometric signal on the basis of at least one of the exercise state or the exercise type; apply the determined weight to the biometric data; acquire biometric data preprocessed by normalizing, on the basis of at least one of the exercise state or the exercise type, the weight-applied biometric data by a designated normalization method; remove noise related to movement of the electronic device from the preprocessed biometric data; and acquire biometric information about the user on the basis of the noise-removed biometric data.

A system is provided.SOLUTION: A system comprising: input means for inputting a training goal of a user and a type of sport; generating means for receiving information from the input means and generating a training plan; capturing and uploading means for capturing a training moving image by the user and uploading the training moving image; analyzing means for analyzing the uploaded training moving image and specifying a motion error and a correction point; and feedback providing means for providing the specified correction point and a specific improvement method to the user.SELECTED DRAWING: Figure 1

The application relates to the field of health equipment management, in particular to a health management equipment data analysis system and method based on the Internet of Things, which comprises a motion detection module, a cluster association module, a state recognition module, a heat monitoring module and an intelligent scheduling module; the motion detection module is used for detecting a motion state, collecting resting data and motion data; the cluster association module is used for generating a motion cluster; the state recognition module is used for judging a current motion type of a user; the heat monitoring module is used for estimating real-time heat consumption; and the intelligent scheduling module is used for detecting a recovery state of the user and matching an appropriate motion cluster. The application can perform personalized analysis on health data of a user, reasonably arrange a daily exercise plan, guarantee a physical state of the user, achieve better exercise effect, help the user record and analyze health data, improve detection accuracy of the health data and improve health management efficiency.

The invention relates to a physical health personalized exercise prescription generation method and device based on biofeedback. The method comprises the steps that biological signals such as the heart rate, the blood pressure and the breathing frequency of a user during exercise are obtained, and a pre-trained signal denoising model is adopted for filtering processing to form a biological signal set; performing multi-dimensional recognition and pattern classification on the biological signal set by using a support vector machine algorithm, and extracting a multi-dimensional feature data set containing the cardiopulmonary function intensity, the metabolic efficiency level and the motion stress response; and inputting the data set into a pre-trained physique evaluation classifier to output a quantitative index set, matching the basic exercise parameters based on a mapping relationship between the quantitative index set and a preset physique health standard library, and generating an initial exercise prescription containing the exercise type, the intensity threshold, the duration and the intermittent period parameter. According to the method, objective quantitative generation of the exercise prescription is realized, and the personalized health intervention precision is improved.

Proposed is an exercise support device operating with a weight training equipment. The exercise support device can extract an exercise object exercising on the weight training equipment connected presently to the exercise support device from a video captured by a camera module by analyzing the skeleton data of candidate objects. Through this, when the multiple candidate objects are detected in an exercise video, a user exercising on the weight training equipment which presently operates with the exercise support device can be accurately specified by using an exercise type and the skeleton data of the candidate objects.

This method for determining an activity state comprises: deriving acceleration values based on a reference coordinate system from an acceleration sensor; regarding a case where the maximum value among the magnitudes of acceleration based on the X-, Y-, and Z-axis directions constituting each acceleration value is maintained at 1.2 g or less for a reference time as a static state, and determining a start posture for dynamic activity; receiving angular velocity values based on the reference coordinate system from a gyro sensor to generate a movement signal related to walking information of a subject; when there is no movement signal for a predetermined time or while a movement signal is being generated, receiving three-axis angular velocity values based on the reference coordinate system from the gyro sensor to track angle change values for the torso of the subject; and determining the exercise type of the subject using the angle change values.

The application relates to a deep knowledge tracking method and system applied to multi-element programming exercises, which comprises the following steps: receiving user exercise data and compilation log data of a programming platform system, and performing data preprocessing; performing log mode clustering on error information; constructing an exercise type feature matrix; performing position coding based on a time interval and learning ability; performing one-hot coding transformation on an answer interaction sequence to obtain an answer interaction embedding vector; and inputting the answer interaction embedding vector into a trained deep knowledge tracking model to realize deep knowledge tracking. Through data processing, knowledge base establishment and model improvement, the model can be applied to multi-element exercises, is more suitable for actual scene application, and especially through establishment of a compilation result knowledge base, application of knowledge tracking to various different exercise types in the programming platform field is realized.

A system for providing a customized health management service using AI-based image analysis is provided, the system comprising: an exercise facility terminal that recognizes entry and exit of a user; a user terminal that records the types and amounts of exercises performed by the user and provides results of analyzing exercise postures according to the types of exercises of the user; and a health management service provision server including a reception unit that receives entry and exit events of the user from the exercise facility terminal, an input unit that receives uploaded images input from at least one camera associated with the exercise facility terminal, a recording unit that searches for and identifies the user in the images, records the types and amounts of exercises performed by the user, and provides same to the user terminal, and a correction unit that transmits, to the user terminal, results of analyzing the exercise postures.

A dynamic cable-actuated resistance training device comprises a base having first and second side pods at opposite ends of the base. First and second motors are located in the first and second sides pods respectively, and cables attached to the motors exit the side pods vertically to provide exercise resistance. Exercise-level tensions in the cables are only applied at or above a preset force-start point, which is at a certain height above the base, of the ends of the cables. The height may be based on a number of factors including the user's height, body proportions, exercise type, and may also be modified by the user.

1. Name of the product in this design: Graphical User Interface for Gym Data Statistics for Electronic Devices. 2. Application of this design: This design is intended for use in an electronic device. 3. The key design features of this product are its graphical user interface content. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: An interactive interface used to display gym exercise data statistics. 7. Graphical User Interface (GUI) Status Description: This section displays relevant information about gym workout data. The central area of ​​the main view in Design 1 shows a leaderboard of participants for the week, ranking them by their respective workout types. The avatars and workout data for different rankings change as the statistics change. The left-hand information bar in the main view displays gym usage, including the male-to-female ratio of users, the number of people using the gym that day, the times employees use the equipment, and the actual usage of the equipment. The displayed graphics also change as the statistics change. The right-hand information bar in the main view displays information about the exercise equipment. The relevant data, such as the daily usage time of exercise equipment, equipment operation status, the ratio of aerobic and anaerobic equipment, and the ranking of equipment usage rates, are displayed in graphs that change as the statistical data changes. Design 1 Interface Change State Figure 1 is the interface that appears after waiting for a certain period of time in the Design 1 main view, or after sliding the middle area. At this time, the middle area displays different rankings depending on the type of exercise shown. Design 1 Interface Change State Figure 2 is the interface that appears after waiting for a certain period of time in Design 1 Interface Change State Figure 1, or after sliding the middle area. At this time, the middle area is a bulletin board for displaying related activities. The middle area in the Design 2 main view... The central area displays a leaderboard showing the activity levels of participants this week in their respective exercise types (e.g., aerobic or strength training). The avatars and activity data for different rankings change as the statistics change. The left-hand information bar in Design 2's main view displays gym usage data, including the male-to-female ratio of users, the number of people using the gym that day, the times employees use the equipment, and the actual usage of the equipment. The displayed graphics also change as the statistics change. The right-hand information bar in Design 2's main view displays data related to the exercise equipment, such as the daily usage time, equipment operation status, and aerobic / intensity training. The percentage of oxygen equipment and the ranking of equipment usage rates are displayed in graphs that change as the statistical data changes. Design 2 Interface Change State Figure 1 is the interface shown after clicking the switch button in the upper right corner of the middle area of ​​the Design 2 main view. At this time, the middle area switches from displaying the information graph of the sports personnel ranking to the statistical graph of sports equipment for one type of sports, such as aerobic exercise equipment. Design 2 Interface Change State Figure 2 is the interface that appears after waiting for a while after Design 2 Interface Change State Figure 1 or after sliding the middle area. At this time, the middle area displays the statistical graph of related equipment for another type of sports, such as strength training equipment.

The present application relates to the technical field of heart rate detection, and particularly relates to a heart rate detection method based on exercise power, comprising collecting radar signals and heart rate signals under different exercise states; performing spectrum extraction of the radar signals at different distance values, calculating the volume of a body part by using the area of the body part, and obtaining the mass of the body part through the volume of the body part; calculating the displacement of the body part; calculating the speed and acceleration of the body part; calculating the physical force of the body part by using the acceleration; calculating the physical workload of the body part according to the physical force; converting the physical workload into exercise power; constructing a first LSTM model, inputting the exercise power and the actually collected heart rate into the first LSTM model to obtain a first predicted heart rate; constructing a second LSTM model by taking the exercise power and the first predicted heart rate as inputs, and outputting a second predicted heart rate. The present application estimates the heart rate based on the density, and is susceptible to the problems of individual differences, exercise types and intensities and the like.

The application relates to a sports training scheme generation method and system for limb feature recognition, and relates to the field of sports training. A skeleton topological portrait and a body shape proportion portrait of a target user are collected. A joint number list and a joint standard time sequence coordinate list of a to-be-trained movement type meeting the portrait are searched. When the target user is trained, a joint time sequence deviation coordinate list is obtained by comparing a joint training time sequence coordinate list with the joint standard time sequence coordinate list. A time proportion list of a time point at which a coordinate deviation distance is greater than or equal to a deviation distance threshold value is counted by traversing the joint time sequence deviation coordinate list. A joint number set at a time point at which a time proportion is greater than or equal to a time proportion threshold value is extracted, an abnormal action set is obtained by inputting the action matching library to execute abnormal action configuration, and a sports training scheme is configured. The technical problems that sports training ignores individual body differences, resulting in insufficient monitoring accuracy and poor individualization of sports training schemes are solved.

The invention discloses a personalized exercise plan generation system based on a user portrait and machine learning, and the system comprises a user portrait construction module which is used for collecting and processing basic attribute data, exercise historical data and real-time exercise monitoring data of a user, so as to construct a dynamically updated user portrait; the exercise plan recommendation engine is based on a machine learning model, is accessed to the user portrait construction module, and is used for generating a personalized exercise plan including an exercise type, duration, intensity and a route according to the user portrait; and the plan presentation and interaction module is used for outputting the generated personalized exercise plan to a user terminal and receiving feedback of a user to optimize subsequent recommendation. According to the method, the dynamic user portrait is constructed, intelligent analysis and recommendation are performed by using the machine learning model, and a scientific, accurate and practical personalized exercise plan can be generated for the user.

The invention relates to the technical field of terminals, in particular to an exercise guidance method, terminal equipment and a readable storage medium. According to the method, before a user starts to exercise based on a target exercise type, terminal equipment can obtain historical exercise scores of the user corresponding to the target exercise type according to historical exercise data of the user corresponding to the target exercise type in a historical period, and the historical exercise scores of the user corresponding to the target exercise type are obtained according to the historical exercise data. The target promotion rate corresponding to the sports performance can be determined through the user portrait data (such as the continuous sports duration of the user corresponding to the target sports type and the like). And calculating a target exercise score corresponding to the target exercise type through the target improvement rate and historical exercise scores (such as historical optimal scores, historical average scores and the like). Therefore, before the user selects the target exercise type and starts to exercise each time, the scientific target exercise score suitable for the user to break through can be determined according to the historical exercise data of the target exercise type corresponding to the user and the user portrait data.

The invention relates to the technical field of crossing of machine vision and motion guidance, in particular to a motion action standard guidance method based on computer vision, which comprises the following steps of: firstly, acquiring an image frame sequence with a timestamp, and extracting actual physical coordinates of key points related to force application; the method comprises the following steps: firstly, generating a force, then quantifying the force through'speed-acceleration deduction '(dynamic motion) or'displacement variance + attitude deviation calculation' (static motion) according to motion types, and finally, generating guidance suggestions in combination with a standard force interval, so that accurate quantification and targeted guidance of the force are realized, and force evaluation requirements of different motion types are met.

A wall device is provided. The wall device may include: an acquisition unit for acquiring a type of exercise selected by a user; a display unit for displaying guide information matching the type of exercise; a providing unit for providing driving information matching the type of exercise to a motion wall; and a control unit for analyzing motion information acquired from the motion wall and analyzing the user's exercise results based on the analysis results.