38 results about "Training load" patented technology

The invention discloses a personalized physical training load intelligent regulation and control system based on multi-modal physiological data fusion, and relates to the technical field of intelligent physical training and health monitoring. The system comprises a multi-modal data acquisition module, an intelligent fusion analysis module, a personalized decision module and a real-time regulation and control module. The method comprises the following steps: acquiring physiological, motion and environmental parameters of a user, and performing multi-modal data fusion by using a time sequence-causal attention mechanism to generate a comprehensive physiological state feature vector; a dynamic personalized baseline is established based on a meta-learning framework, a load adjustment decision is generated by adopting a hierarchical reinforcement learning algorithm, and regulation and control safety is ensured through causal reasoning verification; and finally, load adjustment is executed through the real-time regulation and control module to form closed-loop control. According to the method, the problems that in the prior art, training load regulation depends on a single physiological parameter, the individuation degree is insufficient, and decision making is lack of scientificity are solved, and accurate, self-adaptive and safe individualized physical training management is achieved.

The invention discloses a physical training load regulation and control method and system based on self-adaptive physiological coupling, and the method comprises the steps: collecting an electrocardiosignal, a light volume pulse signal, a respiration signal, a surface electromyographic signal and a body surface temperature signal, and generating a multimode sequence with a time mark; self-adaptive time alignment is executed, a timestamp graph is constructed, clock drift estimation is carried out, segmented monotonic reversible time transformation is established, and a multimode sequence is mapped to a unified time axis; reconstructing the missing section and the artifact section, establishing a mask auto-encoder with physiological priori constraints, combining time domain consistency constraints and frequency domain consistency constraints, and outputting a continuous differentiable reconstructed sequence. According to the method, by introducing adaptive time alignment, cross-modal signal reconstruction and dual-time scale state modeling, coordinated recognition and dynamic load regulation and control of multi-source physiological signals are achieved, coupling analysis in the training process is more stable, and an individual model can be continuously and adaptively updated.

The invention provides a multi-mode biofeedback-guided training intensity adjusting method in the pelma rehabilitation process, and belongs to the technical field of biofeedback guidance. A pressure sensor and a surface electromyographic electrode are fixed to the pelma surface of a patient, and a pelma pressure distribution signal and a lower limb muscle group surface electromyographic signal are synchronously collected; filtering, denoising and rectifying preprocessing are carried out on the collected signals, a real-time biological feedback adjustment value and a training load adaptability evaluation parameter are calculated through an adaptive intensity adjustment model of a liquid neural network structure, and the training intensity is dynamically adjusted by adopting a gating weighting function according to an adjustment value interval; establishing a training intensity optimization equation set comprising a load distribution equation and a fatigue monitoring equation to calculate an optimal training parameter, and feeding back the adjusted intensity parameter to rehabilitation equipment to realize closed-loop adaptive adjustment. The technical problem that real-time self-adaptive training intensity adjustment based on multi-mode biological signals cannot be achieved in the sole rehabilitation training process is solved.

This invention discloses a method and system for constructing a data chain for a scientific fitness guidance platform, relating to the field of medical and health information technology. The method collects multi-source data, including users' basic physical attributes, medical and health data, physical fitness indicators, exercise intensity, and training completion, and performs timestamp alignment and unified access to construct a continuous and complete fitness data chain. It calculates the exercise load safety coefficient, exercise ability adaptation coefficient, and stage intervention effect coefficient, and sequentially constructs mechanisms for exercise load safety assessment, exercise ability adaptation analysis, and stage intervention effect assessment to dynamically determine exercise safety risks, training program matching degree, and stage intervention effectiveness. Furthermore, it automatically generates collaborative adjustment strategies for training load, training structure, and stage parameters, and uses the adjustment results as initial conditions for the next stage of training and data collection, achieving closed-loop feedback control, thereby improving the safety, adaptability, and continuous effectiveness of fitness guidance.

The invention provides intelligent resistance training equipment and method. The method comprises the following steps: synchronously collecting resistance signals of a neck and lower limbs when a user executes a collaborative resistance action; determining force application delay time between the neck and the lower limbs according to the resistance signals of the neck and the lower limbs; according to the angular velocity component of the neck of the user and the anti-resistance signal of the neck, determining a posture-force coupling coefficient when the user executes the collaborative anti-resistance action; determining a load adjustment coefficient of a lower limb target load based on the force application delay time and the posture-force coupling coefficient in combination with a preset load adjustment rule; and adjusting the lower limb load applied by the equipment according to the load adjustment coefficient, and generating prompt information containing posture coordination abnormity at the same time. By adopting the scheme of the invention, the training load conforming to the current coordination capability of the user can be dynamically allocated.

The utility model discloses an assembly capable of adjusting the hanging height of a barbell rod and fitness equipment. The assembly comprises a barbell guide rod, a sliding piece, a barbell rod, a mounting column and a hanging bolt assembly. The barbell rod is coupled with the barbell guide rod through a sliding piece, and the barbell rod is connected with two hooks; a plurality of positioning holes are formed in the mounting column; the hanging bolt assembly comprises a U-shaped supporting piece, a positioning part combined with the U-shaped supporting piece, an auxiliary supporting part and a hanging bolt. The U-shaped supporting piece is operably mounted at different heights of the mounting column through the matching of the positioning part and the positioning hole and the matching of the auxiliary supporting part and the mounting column; the barbell rod is rotated to enable the hook and the hanging bolt to realize hook-bolt connection, so that the barbell rod is hung at a fixed position. The fitness equipment is composed of a frame, the assembly and a balance weight assembly, and the balance weight assembly is used for flexibly adjusting training loads. According to the structure, the manufacturing cost of the fitness equipment is reduced, a user can conveniently and independently use the fitness equipment, and meanwhile use flexibility is kept.

The invention discloses an individualized intelligent reminding method and system for riding training management, and the method comprises the steps: determining an intraday individualized tolerance boundary which is used for limiting the relation between physiological load and training intensity based on user basic data, historical training records and multi-source state data obtained in a preset collection stage; real-time streams of multi-source state data are obtained in the riding process, normalization processing is conducted on the real-time streams according to context factors related to the environment and working conditions, and standardized state vectors are formed in a unified evaluation space; estimating the residual training load according to the standardized state vector; determining a target operation type and a trigger condition thereof according to a preset decision rule; and after the riding is finished, updating the parameters of the individualized tolerance boundary and the decision rule on the current day based on the response of the user to the prompt, and using an updating result for subsequent riding. According to the technical scheme, the safety and efficiency of riding training are improved.

1. Name of the design product: weight-bearing bracelet. 2. Use of the design product: for increasing the training load. 3. Design points of the design product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The present application relates to swimming training equipment technical field, disclose a kind of swimming training device with flow rate self-adapting regulation function and control method.The device includes pool body, partition plate, adjusting mechanism and propulsion module;The upper side of the inner wall of the front end of pool body is provided with water outlet, the lower side of the inner wall of the front end is provided with backwater port, the upper side of the inner wall of the rear end is provided with pool tail drain port;Partition plate is set in the inside of pool body, for dividing pool body into upper training water area and lower function water area, one end of partition plate close to pool tail drain port is provided with throttling window component;The water outlet end of propulsion module is communicated with water outlet, suction end is respectively communicated with backwater port and pool tail drain port, to constitute pool tail bypass backflow path and lower backflow path.Adjusting partition plate position and throttling window component opening, to adjust the tail discharge capacity of training main flow and training load.The present application can improve the problem that pool tail discharge is not timely under short pool body working condition, and it is easy to form stagnation and backflow interference.

The utility model discloses a kind of body-building equipment of high-strength recovery training belongs to body-building equipment technical field.A kind of body-building equipment of high-strength recovery training, including frame, counterweight swing lever being arranged in frame, frame has control area and counterweight area, counterweight swing lever one end is movably connected in control area, counterweight swing lever other end extends to inside counterweight area, counterweight adjusting part is hinged with counterweight swing lever;Flexible connecting piece, winding part for winding flexible connecting piece is rotatably arranged in control area, one end of flexible connecting piece away from winding part is connected between the movable end and hinged end of counterweight swing lever;The utility model can swing with swing arm and converter by arm pressing down pressing rod, flexible connecting piece can be wound to realize the lifting of counterweight swing lever in swing process, realize training with load, after counterweight adjusting part, it is convenient to adjust the size of training load, can realize independent exercise operation, and has positive influence on patient consciously exercising.

The invention discloses a health suggestion generation method and device, a medium and a program product, and relates to the technical field of sports health data calculation and intelligent evaluation. The health suggestion generation method comprises the following steps: calculating the physical ability of a user on that day according to the training load of the user on that day and the potential physical ability of the user, and calculating the fatigue of the user on that day according to the fatigue of the user on that day and the training load of the user on that day; determining a user training state value and a fusion coefficient according to the ratio of the user's physical ability on the day to the user's fatigue on the day and the user portrait; summing a product value of the user sleep influence value and the fusion coefficient with a user training state value to obtain a user physical state evaluation value; and displaying a health suggestion generated based on the user physical state evaluation value. According to the technical scheme provided by the embodiment of the invention, the health suggestions accurately fitting the actual body state of the user can be given, and the user satisfaction is greatly improved.

The invention discloses a cerebral apoplexy lower limb intelligent rehabilitation training system, which relates to the technical field of rehabilitation instruments and comprises a walking module, a body fixing bracket, a foot fixer, a leg adjusting module, a waist and shoulder auxiliary fixing module and a training posture recognition and correction module. According to the training posture recognition and correction module, a binocular 3D camera is innovatively adopted to collect data, a Voxel CNN network and multi-constraint energy function optimization are combined, high-precision joint coordinate recognition, joint angle calculation and motion trajectory similarity evaluation are achieved, and a self-adaptive admittance control algorithm based on performance is matched, so that the precision of the robot is improved. The training load and the auxiliary force can be dynamically adjusted in real time according to the rehabilitation ability of a patient, customized rehabilitation training of one person and one strategy is achieved, the one-step training mode of a traditional instrument is thoroughly changed, rehabilitation training better meets the individual requirements of the patient, and the rehabilitation effect and the training efficiency are remarkably improved.

This application relates to the field of intelligent motion analysis technology, and discloses a multi-dimensional data-driven method and system for volleyball teaching effect feedback. The method includes: collecting real-time training motion and ball-hitting trajectory data to form a raw dataset; extracting features from the raw dataset and analyzing the training motion chain to obtain motion analysis results; if the motion deviation exceeds a threshold, classifying the data to determine a problem classification label; combining the problem classification label with subjective feelings and training load data to generate a personalized feedback report; monitoring subsequent training data according to the feedback report and updating the motion analysis results; comparing the motion analysis results before and after the update to calculate a continuity evaluation index; and generating a visualization chart based on the continuity evaluation index to determine a personalized training optimization path. This application solves the problems of delayed feedback, fragmented data, and lack of continuous evaluation in traditional volleyball teaching, improving the accuracy of volleyball motion diagnosis, the personalization level of training programs, and overall teaching efficiency.

This utility model discloses a tongue tip trainer for speech and oral motor rehabilitation, including a support and a tongue muscle elastic training component. The support has a training space; the tongue muscle elastic training component includes a tongue tip component and an elastic component. The tongue tip component is located within the training space and is connected to the support via the elastic component. When the user's tongue presses against the tongue tip component, the tongue tip component moves within the training space, forcing the elastic component to undergo elastic deformation, thereby providing motion resistance. The tongue tip trainer for speech and oral motor rehabilitation provided by this utility model utilizes the pressing action of the tongue against the tongue tip component during use, enabling the tongue tip component to move within the training space and simultaneously forcing the elastic component to undergo elastic deformation, thus providing motion resistance. This motion resistance can improve the tongue muscle training effect. Furthermore, it can provide variable training loads to meet the different oral motor rehabilitation needs of various users, thereby effectively improving training results and satisfying diverse training requirements.

The present application belongs to the field of medical care information technology, and particularly relates to a movement risk assessment method based on machine learning, wherein historical data and injury records are arranged to obtain a data set, high correlation indexes related to various movement injuries are obtained through feature screening, and independent regression models are established between the high correlation indexes and the risk of various injuries; the physical quality and physiological index system data of a new training personnel group are obtained through testing, the regression models are used to predict the injury risk indexes of each part, the training personnel are grouped and trained through K-means clustering algorithm according to the injury risk indexes; and the historical training load data, functional action screening scores and the next day training plan content are fused to construct a model to evaluate the injury risk index of the training personnel on the next day, so that the present method is suitable for large-scale training groups, the index dimensions are comprehensive and targeted, and the accuracy and efficiency of risk assessment are improved.

This invention relates to the field of sports assessment, specifically disclosing a method and system for assessing post-training physical recovery. It includes: collecting a user's physiological indicators; determining the user's current physical state parameters based on the physiological indicators; obtaining the training load parameters corresponding to the user's current training cycle; calculating the physical fitness value for the current training cycle using a preset fitness correction model; calculating the free energy for the current training cycle using a preset free energy calculation model based on the fitness value, physical state parameters, and training load parameters; and assessing the user's recovery status based on the free energy, a preset fitness baseline, and the physical state parameters, outputting the corresponding recovery status assessment result. This invention introduces the thermodynamic free energy model into the field of athlete recovery assessment, combining it with the principle of supercompensation in exercise physiology to construct a model based on the laws of human energy metabolism, resulting in assessment results that more closely reflect the actual fatigue and recovery patterns of the human body.

The application discloses an adjustable low-oxygen, constant-temperature and constant-humidity training room and relates to the technical field of training room control, which comprises an identity recognition module, an environment initialization module, a physiological monitoring module, a data analysis module, a training load adjustment module and a report generation module; the identity recognition module is used for setting target oxygen concentration, target temperature and target humidity as initial environment control parameters according to the training target type selected by a user on a man-machine interface and preset environment parameter templates, so as to exert specific low-oxygen, constant-temperature and constant-humidity combined training load on the user; the environment initialization module is used for calculating and setting initial oxygen concentration, temperature and humidity parameters according to the training target selected by the user on the man-machine interface and historical data; the physiological monitoring module is used for collecting physiological parameters of the heart rate, blood oxygen saturation, breathing frequency and muscle fatigue degree of the athlete and transmitting the data to the data analysis module for processing; and the data analysis module is used for receiving data from the physiological monitoring module.

The utility model provides a back stretching machine which comprises a base and a support fixed on the base, a seat is arranged above the base, two sets of power-assisted mechanisms are arranged on the support, each power-assisted mechanism comprises a power-assisted arm, a swing arm and a power-assisted pull rod, one end of each power-assisted arm is hinged to the support, and the other end of each power-assisted arm extends in the direction away from the support and is hinged to one end of the corresponding swing arm. One end of the power-assisted pull rod is hinged to the swinging arm, and the other end of the power-assisted pull rod is hinged to the support; the top of the support is hinged with two crosswise distributed tension arms, the top ends of the tension arms are provided with handles, the bottom ends of the tension arms are connected with the power-assisted arms through swing pull rods, one ends of the swing pull rods are hinged with the bottom ends of the tension arms, and the other ends are hinged with the power-assisted arms. The swing arm, the power-assisted arm and the power-assisted pull rod form a power-assisted mechanism capable of realizing force arm change, so that the starting difficulty of the pull-down action can be reduced, the training load can be ensured to meet the training effect, and a user can be better exercised.

A system of estimating acclimatization to environmental conditions and providing a corrected fitness level estimates based on the acclimatization level. The environmental conditions are particularly related to heat and altitude. At first previous training history is provided including a plurality of day records, where each day record includes a date stamp and information regarding altitude and optionally temperature and humidity and training history including timestamp and training load data from executed exercises in stored records.

The invention discloses a jet-propelled swimming pool training load adaptive adjustment method and system, and the method comprises the steps: obtaining user motion posture data, training target data and water pump system water spray parameters, extracting multi-dimensional motion features from the motion posture data, extracting water spray features from the water spray parameters, and carrying out the adaptive adjustment of the training load of a jet-propelled swimming pool. And calculating a real-time training load value based on the two types of features, fusing the real-time training load value and training target data, inputting the fused real-time training load value and training target data into a training load decision model to generate a target training load value, calculating a training load deviation value of the two to obtain a water spraying parameter adjusting quantity, and further controlling a water pump system to adjust water spraying parameters to reduce deviation. According to the method, the dynamic matching of the training load with the real-time state of the user and the training target can be realized, so that the training effect on the user is improved.

We provide technology to predict training effectiveness while taking climate differences into account. [Solution] In the running ability prediction system 100, training load information indicating the exercise load during the user 10's training period, including a first period and a second period in which the climate may differ from that of the first period, is acquired, and climate information for the first and second periods is acquired. The prediction processing unit determines the predicted running time when the user 10 runs a second distance in the second period, based on at least the running log, training load information, and climate information. The running ability correction unit 80 corrects the resulting running ability by a correction amount calculated based on the initial running ability and climate information.

The invention provides an orthopedic rehabilitation training system and method. The method comprises the following steps: acquiring original electromyographic signals and three-dimensional motion data of limbs when a target patient executes orthopedic standard rehabilitation actions; determining an activation time sequence mode of neuromuscles according to the original electromyographic signals, and determining multi-modal motion features based on the activation time sequence mode and the three-dimensional kinematics data; determining a motion function index of the skeletal muscle system in the current rehabilitation period based on the multi-modal motion characteristics, and determining a function recovery track of the skeletal muscle system of the target patient in different training load scenes in the next rehabilitation period; and generating a personalized rehabilitation instruction set according to the motion function index and all the function recovery tracks, and adjusting auxiliary torque output of the rehabilitation training equipment based on the personalized rehabilitation instruction set. By adopting the scheme of the invention, the recovery tracks under different training loads can be predicted through the multi-modal motion characteristics of the patient so as to dynamically generate the personalized instruction set to assist the patient in rehabilitation training.

The invention discloses an exercise training simulation method, system and device based on metabolic capability and a storage medium, and relates to the technical field of exercise physiological modeling and computer simulation, and the method comprises the steps: collecting basic metabolic parameters and exercise physiological data of a training object, and generating a metabolic feature vector; calculating a metabolic reaction rate and predicting energy consumption based on the metabolic feature vector and in combination with the training load parameter and the motion time sequence to obtain a metabolic response sequence under different training load conditions; correlation analysis of a time domain and an intensity domain is carried out on the metabolic response sequence, and the metabolic demand of the target training plan is compared with the metabolic response difference output by simulation; and according to a difference result, adjusting training load parameters and repeating simulation operation to generate a training simulation scheme. According to the method, precise modeling and dynamic simulation of individual metabolism are achieved, the training load is adjusted in a self-adaptive mode through metabolism difference analysis, and the matching degree and safety of the training plan and the actual metabolism capacity are improved.

The invention relates to the technical field of hand function rehabilitation, in particular to a hand function rehabilitation evaluation and training method based on a virtual reality technology, which comprises the following steps: constructing a reference attitude sequence according to an evaluation sample, comparing real-time attitudes frame by frame to extract a difference trend, recording a turn-back path and a turn-back frequency, and calculating a redundant path and a stability index. According to the method, by accurately constructing a personalized training target and contrasting real-time attitude information frame by frame, the rollback state in training can be recognized in time, redundant paths and stability characteristics can be analyzed, instability in the training process can be quantified, and the training efficiency can be improved. According to the innovative mode, the accuracy of training feedback is improved, a personalized and intelligent rehabilitation adjustment scheme is provided for a patient, rhythm control and tolerance band setting are combined, the effectiveness of a training task closed loop is ensured, blind adjustment of training loads is avoided, and the rehabilitation effect and the pertinence and effectiveness of progress are improved.

A training method on a strength exercise machine is usable by a user to perform a strength exercise, as a series of repetitions, for training the power generable by the user in at least one first workload regimen. In case a determined or detected power peak value is either higher than or equal to a stored maximum-power reference value, the steps of updating the stored target power value as a function of the generated power peak value determined or detected, updating the stored maximum-power reference value to the determined or detected power peak value, and resetting a counter variable value are performed. In case the determined or detected power peak value is lower than the stored reference maximum power value, a step of incrementing the stored counter variable value is performed. The method is performed for each further repetition of the first workout series.

The application discloses a method and device for determining cycling training load, a storage medium and an electronic device. The method comprises: dividing a whole cycling record of a target user into M motion segments, M being a positive integer; determining N characteristic matrices according to data of the M motion segments, N being a positive integer less than or equal to M, and each characteristic matrix comprising at least one of the following: user attribute information, weather information, slope information and speed information; inputting the N characteristic matrices into a target model for processing to obtain training loads of the N motion segments, the target model being trained by a plurality of sets of sample data, each set of sample data comprising a sample characteristic matrix and a sample training load; and accumulating the training loads of the N motion segments to obtain a total training load of cycling of the target user. Through the application, the problem that it is difficult to obtain heart rate data of a user who does not use a wearable product and thus it is difficult to calculate a training load of the user during exercise in the related art is solved.

This invention provides a bed-mounted integrated intelligent rehabilitation training system and its rehabilitation training method, belonging to the field of rehabilitation training technology. It includes an integrated bed-controlled support platform with an electrical interface, a lower limb servo stress loading mechanism mounted on the platform, upper limb multi-dimensional feedback training mechanisms on both sides of the platform, a multimodal state sensing network, and a central intelligent processing terminal. The lower limb mechanism can apply axial mechanical stress to the lower limbs and drive movement training, while the upper limb mechanism provides upper limb joint mobility and resistance training. The sensing network collects training load, joint angle, and movement trajectory parameters. The central intelligent processing terminal communicates and manages each mechanism and executes training prescriptions, performs safety monitoring, and provides feedback control. The beneficial effects are that bedridden patients can conduct coordinated upper and lower limb rehabilitation training without leaving the bed, breaking the limitations of fragmented training; multimodal real-time sensing combined with terminal closed-loop control improves the accuracy of training load and safety response capability, ensuring safe and efficient early active rehabilitation for bedridden patients.

The application discloses a method and device for determining a training state, which comprises the following steps: obtaining physiological data, wherein the physiological data comprises physical fitness data, body state data and training load; the physical fitness data is used for representing the physical fitness intensity of a human body; the training load is used for representing the fatigue degree of the human body caused by training with different intensities; determining a physical fitness level according to the physical fitness data, determining a body state level according to the body state data, and determining a training load level according to the training load; determining the training state according to the physical fitness level, the body state level and the training load level; and outputting prompt information of the human body movement ability according to the training state. The method provided by the application can scientifically and effectively evaluate the current training state and the current movement ability of a user.

The application discloses a rehabilitation training method and device based on virtual reality technology, and relates to the field of virtual reality rehabilitation technology.The method comprises the following steps: after completing the activity range and corresponding orientation calibration, generating rehabilitation training tasks with different difficulty coefficients; the rehabilitation training task is continuous target tracking, specifically, in response to a user control request, controlling a ring-shaped cursor to move along the movement track of a target ball, so that the target ball is kept in the ring-shaped cursor; difficulty coefficient calibration is performed on the training load of the rehabilitation training task according to system parameters of the rehabilitation training task, wherein the system parameters comprise the complexity of the movement track of the target ball, the radius of the target ball, the radius of the ring-shaped cursor, the error range, the fixed effect factor and the relative effect factor; and the rehabilitation training task with corresponding system parameters is executed according to the required difficulty coefficient.The application can realize accurate regulation and control of the training load, and is suitable for rehabilitation training requirements of different recovery degrees.

The invention belongs to the technical field of medical care information, and particularly relates to an exercise risk assessment method based on machine learning, and the method comprises the steps: carrying out the arrangement of historical data and injury records, obtaining a data set, carrying out the feature screening, obtaining a high-correlation index of each exercise injury, and building an independent regression model for the high-correlation index and each injury risk; testing the new training personnel group to obtain physical quality and physiological index system data of the new training personnel group, predicting injury risk indexes of all parts of the new training personnel group by adopting a regression model, and grouping and training the training personnel through a K-means clustering algorithm according to the injury risk indexes; according to the method, the historical training load data, the functional action screening score and the next-day training plan content are fused, the model is constructed to evaluate the next-day injury risk index of the training personnel, the method is suitable for large-scale training groups, the index dimension is comprehensive and targeted, and the risk evaluation precision and efficiency are improved.