220 results about "Lower limb exercises" patented technology

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention discloses a lower limb muscle fatigue assessment method and system based on multi-modal physiological signals, and relates to the technical field of biomedical engineering, target muscles are selected, sEMG signals, AUS signals and respiratory flux VE data are synchronously collected, and RPE is recorded; preprocessing various signals to remove interference and abnormal points; extracting sEMG time-frequency domain features and AUS muscle thickness features; fusing features and constructing vectors in a standardized manner; dividing five fatigue grades; and a data set is divided according to stratified sampling, a model is optimized through five-fold cross validation, and real-time features are input to output a fatigue level. Multi-modal physiological signals are fused, fatigue evaluation accuracy is improved, different fatigue stages are finely adapted, muscle load and energy consumption are reduced through personalized adaption and a dynamic adjustment strategy, rehabilitation training safety and comfort are improved, and efficient recovery of lower limb movement functions is assisted.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention discloses a Parkinson's disease gait analysis and diagnosis method based on deep learning, and belongs to the technical field of medical signal processing. The method comprises the following steps: acquiring multi-modal time sequence gait data of Parkinson's disease, preprocessing the multi-modal time sequence gait data, and dividing a data set to obtain preprocessed multi-modal time sequence gait data; the multi-modal time sequence gait data at least comprises foot pressure data, lower limb motion acceleration and lower limb swing angular velocity; designing a feature extraction network structure of a multi-channel convolutional neural, extracting features from the multi-modal time sequence gait data, and obtaining useful features from local to global; establishing a Transform feature fusion module, and introducing a multi-head self-attention mechanism to process all input useful feature data in parallel; a softmax classification diagnosis module is provided, Dropout and regularization technologies are adopted to avoid overfitting of data, an Adam optimizer is utilized to optimize model parameters, and a Parkinson's disease gait analysis diagnosis result is obtained.

The invention discloses a control method, and the method comprises the steps: collecting the data of a centroid state and an MPC optimal ground acting force in a plurality of motion states of a robot, designing a full-connection residual network, taking the centroid state information as the input, taking the ground acting force as the output, and designing a loss function based on priority. Upper and lower bounds of a friction cone and a sole reaction force are constrained, and the network is trained offline to fit MPC optimization quantity, so that an efficient mode of online reasoning is realized; the joint angle and the angular velocity are fused with the action at the previous moment, the centroid linear velocity and the angular velocity and the foot-ground acting force fitted through a residual network, a bionic reference trajectory is generated through a human motion public data set, different weights are given to a reward function group according to a dominant function value obtained through calculation, and the control performance is improved; symmetrical loss constraint is added to lower limb movement, and the algorithm convergence speed is increased. And finally, training to obtain a whole-body control strategy based on model priori knowledge and a human motion reference trajectory.

The invention discloses a weight loss gait test evaluation and training device and method, and the device comprises a weight loss control module which is used for weighing the weight of a user in real time and automatically adjusting the air pressure of an air bag according to a set weight loss proportion; the gait information acquisition module is used for synchronously acquiring plantar pressure distribution, lower limb joint kinematics parameters and double-view video data; the multi-modal gait evaluation grading model module is used for processing plantar pressure distribution, lower limb joint kinematics parameters and double-view video data and outputting lower limb dyskinesia grades; and the training scheme generation model module is used for automatically generating a training scheme including a weight reduction proportion, a treadmill speed, single exercise time and training times per week according to the lower limb dyskinesia level and the personal information of the user.

Disclosed in the present invention is a lower-limb-powered exoskeleton device for rehabilitation assistance, comprising ankle-joint sagittal-plane driving units, knee-joint sagittal-plane driving units, hip-joint sagittal-plane driving units, a hip-joint horizontal-plane driving unit, a joint driver connecting frame, a lower-limb fixing support, sensors, a controller and a power supply. Antagonistic-drive-type linear driving units are used in the sagittal plane of a hip joint so as to enable the bending and extension movements of the hip joint, and a linear driving unit having the function of self-locking when powered off is used in the horizontal plane of the hip joint so as to enable the turning movements of the exoskeleton; and a linear driving unit in cooperation with a magnetorheological brake is used in the sagittal plane of each knee joint, thereby significantly reducing the power consumption of a knee-joint driver while lowering the control difficulty. The powered exoskeleton device can help patients with an impaired lower-limb motor function to carry out walking rehabilitation training.

The invention discloses a multi-dimensional lower limb parameter detection method and system fusing spatial-temporal characteristics, and the method comprises the steps: collecting the lower limb movement information of a wearer through a wearable device; the lower limb movement information comprises position information, posture information and sole multi-dimensional stress information; estimating lower limb space-time parameters and gait events according to the lower limb movement mechanical model and the lower limb movement information; based on the plantar multi-dimensional force measurement model and the lower limb movement information, plantar multi-dimensional force data fused with the spatio-temporal characteristics are obtained. The method can accurately estimate lower limb space-time parameters and human body tracks, and realizes plantar three-dimensional force space resolving and mapping.

The invention discloses a lower limb exercise load assessment method and system based on multi-mode wearable equipment, and the method comprises the steps: 1) collecting exercise signals, three-dimensional track information and actual ground reaction force when a testee runs at different set speeds, and synchronizing the exercise signals, the three-dimensional track information and the actual ground reaction force through timestamps; 2) calculating the tibia force of the testee; 3) training a tibia force and vertical ground reaction force estimation model; 4) inputting the motion signals collected in real time into the tibia force and vertical ground reaction force prediction model to obtain predicted tibia force and vertical ground reaction force; and 5) calculating to obtain a lower limb exercise load evaluation index. The system comprises a pressure insole device, a pressure leg sleeve device, a surface myoelectricity device, an inertial sensor device, a training data acquisition module, a tibia force prediction model construction module, a vertical ground reaction force prediction model construction module, a vertical ground reaction force prediction module, a tibia force prediction module and a lower limb exercise load evaluation module. Compared with other methods, the method provided by the invention has higher systematicness and scientificity.

The invention discloses a multi-sensor motion evaluation method and system for special training of lower limbs. The method comprises the following steps: step S100, collecting data of various sensors of the lower limbs; step S200, based on the collected data, carrying out gait and support state identification; step S300, judging the state of foot varus and valgus; step S400, calculating a foot stability index; step S500, calculating a lower limb exercise ability index; step S600, carrying out special exercise evaluation; and step S700, generating training feedback. Compared with an optical system, the core advantages of the system in training are reflected in that the system can be used for real training instead of a forced performance type test; the device is suitable for long-distance and continuous special training; the technical stability and injury risk can be directly reflected; and the training evaluation cost and the use threshold are obviously reduced.

The invention relates to a lower limb motor function recovery system and method based on a motor imagery brain-computer interface, and the system comprises a noninvasive electroencephalogram signal collection module which is used for collecting an electroencephalogram signal when a patient carries out a lower limb motor imagery task; the signal processing and intention recognition module is used for carrying out feature extraction and classification recognition on the collected electroencephalogram signals and outputting the motor imagery intention of the patient; the multi-sensory feedback module is used for providing multi-sensory feedback information including visual sense, auditory sense, tactile sense and proprioceptive sense for the patient; the lower limb rehabilitation training module is used for executing lower limb rehabilitation training actions corresponding to the motor imagery intention; the central control module is configured to receive the recognition result of the motor imagery intention and generate a control instruction according to the recognition result to drive the multi-sensory feedback module and the lower limb rehabilitation training module to work cooperatively. According to the system, a multi-sensory collaborative feedback mode is adopted to simulate multi-sensory interaction, and the identification degree and training compliance of a motor imagery task of a patient are improved.

The invention discloses a multi-modal sensing data automatic labeling method and system for gait assessment, and the system comprises a sensing collection module which comprises two paths of pressure sensors and a six-axis IMU sensor; the two paths of pressure sensors are used for acquiring plantar pressure data; the six-axis IMU sensor is used for acquiring lower limb movement data; the wireless transmission module is used for uploading the collected plantar pressure data and lower limb movement data to a cloud Internet of Things platform; the cloud data processing module is used for receiving and storing the plantar pressure data and the lower limb movement data; the data labeling module is used for automatically recognizing a gait cycle and carrying out multi-dimensional score labeling; and the user interaction module is used for interacting with the data annotation module and the cloud data processing module and displaying gait evaluation related data and results. According to the invention, a multi-mode sensing PCB is self-developed, a closed-loop system of collection, cloud loading, labeling and display is constructed, and accurate, continuous and remote evaluation of gaits of children with cerebral palsy is realized.

The invention relates to a closed-loop control method suitable for personalized lower limb exercise training, belongs to the technical field of exercise rehabilitation, and designs a closed-loop control method for dynamically adjusting lower limb training load according to muscle activation degree by combining surface myoelectricity characteristics with a personalized training model and a closed-loop control algorithm. According to the method, the resistance load can be adjusted in real time according to the muscle state, and finally the muscle activation level in the training process is maintained in an expected range. In the pre-training stage, sEMG data are collected to establish a personalized mapping model, and response characteristics of individual muscles are quantified; in the formal training stage, the muscle state is monitored in real time, the reference load is predicted through the model, and a closed-loop control algorithm is adopted to generate dynamic adjustment amount to adjust resistance output in real time. The invention further provides a closed-loop control system suitable for personalized lower limb exercise training.

The application provides a kind of rehabilitation auxiliary device after vascular intervention, including seat plate and exercise mechanism, and the exercise mechanism is symmetrically arranged on both sides of the seat plate, and the exercise mechanism includes pedal, resistance mechanism and protection mechanism.By treading pad, the first spring is compressed and shrunk, and the lock rod is separated from the currently matched lock hole by the first transmission assembly, at this time, treading pad and pedal can be driven to move away from the seat plate to resist the resistance mechanism to pedal and exert the thrust to move close to the seat plate, to achieve the purpose of lower limb exercise, when the foot accidentally slips off from the pedal or the heel is raised, the pressure on the pad disappears, the reaction force of the first spring makes the pad move away from the pedal and drives the lock rod to be arranged in the currently matched lock hole to fix the pedal, so that when the foot accidentally slips off from the pedal or needs to be terminated due to accident, the pedal can be fixed in time, to avoid the pedal rebounding and causing harm to the user, and to improve the safety of the device.

The invention discloses a lower limb multi-joint torque intelligent prediction method and related equipment, and relates to the technical field of joint torque prediction.The method comprises the steps that original surface electromyography sEMG, kinematics data and ground reaction data of a subject under different lower limb movements are collected; kinematics data and ground reaction force data are used as input, and multi-degree-of-freedom angle data and moment data of three joints of the lower limb are obtained through inverse kinematics and inverse dynamics calculation; constructing a lower limb multi-joint torque prediction model through a deep learning technology, and performing model training by using the training set; and inputting the test sample set into the trained lower limb multi-joint torque prediction model to obtain a joint torque prediction result of the subject. According to the method, the problems of low joint torque prediction precision, high calculation cost and the like in the prior art are solved, the method has the advantages of being easy to implement, high in generalization, capable of processing large-scale data and the like, and prediction results are expected to be applied to three-dimensional compliance and modular control of the exoskeleton robot.

The present disclosure provides a movement system. The movement system includes a foot-plate exercise machine and a chair. The foot-plate exercise machine implements repetitive lower limb movement accompanied by extension and flexion of at least a knee joint and a hip joint. The chair is a component for a user to sit on while the user uses the foot-plate exercise machine to perform the lower limb movement. The chair has a seating portion for the user to sit on, and a support mechanism that supports the seating portion in a manner that allows the seating portion to rock laterally. By the seating portion rocking laterally during the lower limb movement, a movement chain of the lower limb and the torso is generated.

The present invention discloses a real-time analysis method and system for lower limb motion data applied to rehabilitation guidance. The method calls a personalized model based on user information, converts real-time motion data into historical motion feature vectors, inputs them into a real-time prediction module, and generates multiple frames of future motion feature vectors through encoding and decoding. The deviation value of each future motion feature vector from a preset threshold range is calculated to obtain a motion deviation value, and the motion risk ratio of each future motion feature vector is calculated. The motion risk probability is obtained through weighted fusion, and the device parameter adjustment value and posture adjustment information are generated through reverse mapping. Finally, the training stage is advanced based on the risk probability assessment, and correction information is synchronously displayed and the device operating parameters are adjusted. This method constructs a dynamic model through pre-training and combines it with a lightweight LSTM to achieve real-time prediction of future motion features, thereby improving the predictability and personalized adaptation capabilities of rehabilitation guidance.

The invention discloses lower limb dyskinesia adjusting equipment and a gait adjusting method, and the equipment comprises a wearing part which comprises a housing matched with a lower limb; the projection module comprises a projector arranged on the shell, and the projection angle of the projector is adjustable; the control unit comprises a control mainboard arranged in the shell and an acquisition module communicating with the control mainboard, and the acquisition module is configured to acquire gait data of the wearing piece in real time; the projector communicates with the control mainboard, and the control mainboard is configured to control and adjust the projection angle of the projector based on the gait data acquired by the acquisition module. The lower limb dyskinesia adjusting device prompts gait disorders by using vision, and the mechanism is that the lower limb dyskinesia adjusting device can provide external sensory cues for the patient and stimulate the visual system of the patient, so that neural activity of the premotor area is activated. Visual information is used for guiding gaits, and the deficiency of internal movement rhythm caused by basal ganglion dysfunction is made up.

The invention relates to the technical field of training instruments, in particular to an intelligent heart rehabilitation training assisting device which comprises a supporting frame, a first supporting rod is fixedly connected to the top of the supporting frame, a second supporting rod is fixedly connected to the right side of the supporting frame, and a cushion is fixedly connected to the upper end of the second supporting rod. A lower limb exercise mechanism is arranged in the supporting frame, an upper limb exercise mechanism is arranged in the first supporting rod, and a linkage assembly is arranged between the lower limb exercise mechanism and the upper limb exercise mechanism. Through the arrangement of the linkage assembly, the exercise requirement is small in the initial rehabilitation stage of a patient, lantern rings at the positions of connecting ropes can be connected to clamping grooves of a handle and a pedal in a sleeving mode, combined training is achieved between the lower limb exercise mechanism and the upper limb exercise mechanism, physical output is reduced, and therefore the more effective recovery effect is achieved on rehabilitation training of the heart of the patient; by arranging the adjusting assembly, the resistance is increased, the exercise amount is increased, and the exercise intensity of the patient is adjusted.

The embodiment of the present disclosure provides a lower limb motion state recognition model training method and a lower limb motion state recognition method. The model training comprises: determining the sorting order of the binary classification models corresponding to various lower limb motion states, and sequentially training the binary classification models according to the sorting order to obtain model parameters and current classification results of each sample data; and training a result decision model according to the binary classification results obtained by processing the sample data by the binary classification models corresponding to all lower limb motion states and the lower limb motion state labels of the sample data, and determining the model parameters of the result decision model. Through the reference of the previous binary classification result output result, the correct result of the subsequent binary classification model can be realized as much as possible, the model accuracy and robustness of the subsequent binary classification model are improved, and then the accuracy and robustness of the entire lower limb motion state recognition model are improved.

The invention discloses a motion intention recognition method based on multi-modal enhanced comparative learning, and the method achieves the precise motion intention recognition based on any single mode through systematic fusion of multi-modal physiological signals and an innovative comparative learning architecture. Intention perception contrast learning: constructing positive and negative sample pairs enhanced by a multi-modal time sequence and optimizing contrast loss, so that the discrimination capability of the model in dynamic intention transfer is remarkably improved; according to many-for-one cross-modal prediction, multi-modal identification performance under single-modal input is realized through semantic alignment and a knowledge migration mechanism. After pre-training joint optimization, training and reasoning can be completed only through single-modal data, and data dependence is remarkably reduced while the advantage of multi-modal performance is kept. According to the technical scheme, the accuracy and robustness in lower limb movement intention recognition in the brain-computer interaction field are effectively improved, and an innovative technical solution is provided for a movement auxiliary rehabilitation system.

The present invention discloses an assistive and support device for exercising lower limbs, which has support posts provided with fastening clamps on the rear face thereof, which allow the device of the present invention to be adapted to various pieces of exercise equipment. Said support posts are vertical structures with a hollow interior that house axles on which toothed straps are mounted and which move in a synchronised manner with the movement of a motor. This configuration makes it possible to have a support during weight training, an activity that can be safely performed as it is provided with a locking mechanism. The device of the present invention has sensors to monitor the electrical activity produced by the skeletal muscles to detect the user's effort in order to prevent injuries during exercise.

The invention discloses an apparatus facilitating lower limb movement and learning, and belongs to the technical field of sports apparatuses, the apparatus comprises a first connecting seat and a second connecting seat, a lower limb movement mechanism is arranged between the first connecting seat and the second connecting seat, and the top of the lower limb movement mechanism is fixedly connected with a learning mechanism; the learning mechanism comprises a first connecting plate fixedly connected with the lower limb movement mechanism, a second connecting plate is fixedly connected to the top face of the first connecting plate, a third connecting plate is slidably connected to the side, away from the first connecting plate, of the second connecting plate, a limiting part is arranged between the second connecting plate and the third connecting plate, and a first groove is formed in the top face of the third connecting plate. And a learning part is arranged in the first groove. According to the invention, synchronous movement and learning can be realized, the lower limb movement mechanism provides scientific exercise, the learning mechanism supports multi-scene adjustment, a third connecting plate for sliding adjustment adapts to users with different heights, and the health management requirements of teenagers sitting for a long time are met.

The utility model relates to the field of rehabilitation training instruments, in particular to a rehabilitation device for myasthenia patients, a rehabilitation part comprises a shell, a groove is formed in the shell, a driving motor is arranged in the shell, the output end of the driving motor is connected with a lifting threaded rod, and the top of the lifting threaded rod is movably connected with the top of the inner wall of the groove; a lifting block is arranged on the surface of the lifting threaded rod and located in the groove, a baffle is arranged on the front face of the lifting block, the front face of the baffle extends out of the groove, a clamping groove is formed in the front face of the baffle, a clamping column is arranged on the inner wall of the clamping groove, a rotating plate is movably connected to the surface of the clamping column, and fixing rollers are arranged on the two sides of the rotating plate. A driving motor can be started to enable a lifting threaded rod to rotate, then a lifting block drives a baffle and a connecting structure of the baffle to conduct height change till the position where a fixed roller is located is matched with the height of a patient sitting on the seat, and when the fixed roller can be arranged to be suitable for upper and lower limb exercise actions, a rotating plate can be pushed to rotate.

The application discloses a control method of an autonomous navigation lower limb robot, which comprises the following steps: collecting human gait data to obtain an original gait curve, optimizing the gait in combination with sensor data, and controlling an exoskeleton to perform rehabilitation training; based on lower limb motion parameters and navigation instructions, the speed of a driving wheel is controlled, so that the platform advances to a target direction in cooperation with the lower limb motion parameters, thereby realizing the functions of gait training and autonomous navigation, and the comfort during lower limb rehabilitation training and the experience of a patient are improved, and real-time regulation and control of lower limb motion and autonomous control of a walking direction of the patient during training are realized.

This invention belongs to the technical field of rehabilitation robots, specifically relating to a continuous phase control method for a lower limb rehabilitation exoskeleton that adapts to user gait. It employs a multi-task learning model, simultaneously outputting user identity probability and movement pattern probability to generate a probability map representing the similarity between the current gait and a certain movement pattern of a sample user in a sample database. A gait phase variable parameter prediction model is used to predict gait phase variable parameters, employing angular velocity data from the initial stage of the current gait cycle and the matched movement pattern to predict the gait phase variable parameters for the current cycle. Finally, a pre-constructed relational constraint model corresponding to the matched sample user and their movement pattern is used to obtain the desired joint angles from the real-time gait phase, and the exoskeleton is tracked and controlled via a PD controller. This invention can adaptively adjust the exoskeleton's motion assistance mode according to the user's gait characteristics, and is suitable for rehabilitation training of patients with lower limb motor dysfunction.

The application provides a flexible assisted lower limb motion robot, and relates to the field of motion robots, which comprises a thigh motion assembly, a shank motion assembly and a foot connecting assembly, the shank motion assembly is located below the thigh motion assembly, and the foot connecting assembly is located below the shank motion assembly, the motor in the thigh motion assembly controls the contraction and release of each rope, cooperates with the tensioning mechanism, realizes the lifting and lowering of the thigh belts on both sides, and assists in motion; the driving muscle in the shank motion assembly simulates the natural motion process, provides power, and drives the shank belt to move through the rope, so that the shank motion is realized; through the cooperation of the rope and the pulley set in the shank assembly, the distance between the sliding table and the sliding table support is adjusted, the freedom degree in the lower limb motion can be met to a large extent, and through the flexible connection of the spring and the foot connecting frame, the flexible adaptation of the foot to the leg during walking is realized.

The utility model provides a pair of counterweight shoes for postoperative rehabilitation, comprising: a shank fixing sleeve, which is arranged around the shank of a patient, and the outer side wall of the shank fixing sleeve is provided with at least one accommodating bag; the balancing weights are different in balance weight, and the balancing weights are detachably connected into the containing bag; the sea salt patch is detachably connected to the inner side wall of the shank fixing sleeve; wherein the shank fixing sleeve extends in the direction of the heel and is provided with a heel fixing sleeve, and the heel fixing sleeve is used for fixing the heel of a patient. According to the scheme, a medical worker can match the balancing weight with the corresponding balancing weight according to the injury condition of a patient, in the walking process of the patient wearing the balancing weight shoes, the balancing weight shoes can move up and down, then the sea salt patches are driven to massage the shanks of the patient, muscular soreness can be relieved, and blood circulation of the lower limbs can be promoted. Therefore, the lower limb rehabilitation progress of the patient is accelerated. Meanwhile, the counting device counts the lower limb exercise amount of the patient to help the patient to do quantitative exercise.

A method for measuring motion postures of lower limbs of an automobile collision dummy continuously collects lower limb images of the automobile collision dummy during a collision in a real automobile collision test; identifies an edge contour in each lower limb image; samples a plurality of points from the edge contours for fitting to obtain a lateral thigh expression, a lower medial thigh expression, a lateral lower leg expression, and a lower medial leg expression respectively; determines various bone positions, various joint positions, and various bone angles of each lower limb; and associates the various bone positions, the various joint positions, and an included angle between two ends of each joint in the plurality of lower limb images in a chronological order to form a series of two-dimensional human rod-shaped model diagrams.