23 results about "Joint angulation" patented technology

This application discloses a training method, apparatus, device, and storage medium for a joint angle prediction model, relating to the field of computer technology. The method includes: acquiring training sample data, which includes skeletal parameters, sample wrist joint pose sequences, and arm angle labels for the sample wrist joint pose sequences; obtaining arm angle feature information based on the sample wrist joint pose sequences using a selective state-space network of the joint angle prediction model to be trained; obtaining elbow position prediction information based on the arm angle feature information and skeletal parameters using a differentiable kinematic layer of the joint angle prediction model to be trained; and iteratively updating and training the joint angle prediction model to be trained based on a preset objective function, the training sample data, the arm angle feature information, and the elbow position prediction information to obtain a trained joint angle prediction model.

This invention relates to a markerless real-time registration and tracking method and system for mandibular surgery. The method includes the following steps: the patient performs multiple opening and closing movements of the upper and lower jaws while the robotic arm is fixed, and the system synchronously records the changes in the angles of each joint of the robotic arm; based on the forward kinematics of the robotic arm, the continuous motion trajectory of the distal end of the jaw in space is calculated to form an actual jawbone trajectory point cloud; a preoperative image model is formed using the actual jawbone trajectory point cloud, a simulated jawbone trajectory is constructed in the preoperative image model, and the actual trajectory is aligned using the ICP registration algorithm to achieve automatic registration; the system establishes a mapping between angle changes and the spatial posture of the jawbone to achieve dynamic tracking and visual navigation during surgery. This invention solves the problems of large errors, high complexity, many limitations, and low efficiency in preoperative image data registration technology during maxillofacial surgery, avoids the need for preoperative implantation of markers, and achieves high-precision registration, high automation, and strong real-time performance under natural conditions.

The application discloses a hip joint angle prediction method based on environment perception fusion, and relates to the fields of artificial intelligence and rehabilitation robots. The method first acquires a depth image and kinematics data, extracts spatial statistical features of the depth image, performs non-uniform segmentation on the depth image, obtains a double-vision feature sequence through sinusoidal position encoding and physical position encoding, fuses the double-vision feature sequence with the kinematics data respectively, generates two angle prediction sequences, and finally performs environment perception fusion to output a hip joint angle. Compared with the prior art, the application has the advantages of adaptive fusion of multiple position encodings, can accurately predict the hip joint angle under variable terrains, and has strong adaptability to environment changes.

This invention discloses a human posture analysis system and method for sports equipment based on big data, belonging to the field of human posture analysis technology. The system includes acquiring joint angle data of the user, the speed generated by the equipment, and the force exertion data of the user while using the equipment; establishing a static posture analysis model based on the joint angle values ​​to generate a static posture score; establishing a dynamic posture analysis model based on the joint angular velocity and the equipment data to generate a dynamic posture score; establishing a comprehensive posture analysis model based on the user's force exertion data, static posture score, and dynamic posture score to generate a comprehensive posture score; and analyzing the human posture based on the comprehensive posture score. This invention solves the problems of limited analytical dimensions and insufficient quantification of human-machine collaboration efficiency in existing technologies, improves the accuracy of motion quality assessment, and provides users with guidance that balances safety and effectiveness.

The articulation angle is an important characteristic to quantify for open loop control and / or closed loop control of a medical instrument comprising an elongated body. The articulation angle may be computed using various techniques including using inverse trigonometric functions on vector representations of elongated body poses. The technique may involve projecting the vectors onto a measurement plane having a normal vector. The technique enable differentiation between positive and negative articulation directions. Additionally, the technique can enable the articulation angle to be computed even for high articulation angles, e.g., more than 180 degrees. The so computed articulation may be used for open loop or closed loop control of the elongated body.

The application discloses a tension amplification constant torque constant stiffness rope-driven humanoid knee joint and belongs to the technical field of robots. The knee joint comprises a knee joint fixed end and a knee joint movable end, the lower end of the fixed end is provided with an upper quarter circular arc stand plate, the upper end of the movable end is correspondingly provided with a lower quarter circular arc stand plate, the two are connected through a double-end connecting piece to form a rotating pair and are opened and closed in contact through unilateral rotation, and a joint rope drive assembly with tension amplification and constant torque is arranged therebetween. The dynamic pulley block mechanism of the rolling joint is used to realize speed ratio amplification and tension amplification, and the demand for the output torque of the motor is reduced; the unique rope arrangement makes the rope length change amount and the joint angle change amount keep a linear relationship, constant torque constant output is realized, and the rope drive and rope transmission structure are adopted to improve the flexibility and safety of the joint. The application realizes lightweight, high integration and flexible and safe interaction of the humanoid robot joint and is suitable for the lower limb knee joint of the humanoid robot.

This invention discloses a method and system for predicting lower limb joint angles, comprising the following steps: collecting first original joint angle time series from multiple healthy test subjects continuously performing various typical movements; collecting second original joint angle time series from multiple healthy test subjects performing any type of typical movement; standardizing the first original joint angle time series to obtain standard samples, wherein a portion of the standard samples constitutes the training set, and the remaining standard samples serve as the first samples; standardizing the second original joint angle time series to obtain second samples; using both the first and second samples as test samples, and all test samples constitute the test set. This invention can effectively improve the prediction accuracy and real-time computation efficiency of the prediction model, and enhances the cross-scene adaptability of the prediction model while ensuring prediction accuracy, facilitating practical use.

The application discloses a singular point avoidance control method and device for a six-axis mechanical arm based on virtual seven degrees of freedom, a virtual rotation axis is arranged at the end of the mechanical arm and coincides with the origin of the end coordinate system and is perpendicular to the rotation axis of the end coordinate system, the control method comprises the following steps: acquiring the position information, angle information of each joint of the mechanical arm and the actual pose and expected pose of the end in the current detection period; based on the position information and angle information of each joint, the weight value of the virtual rotation axis is adjusted correspondingly according to the state of the mechanical arm approaching the singular point of the shoulder and / or the singular point of the wrist, and a quadratic programming problem for avoiding the singular point is reconstructed, and the joint angle increment vector to be executed is solved; based on the joint angle increment vector to be executed, the angle value of each joint of the mechanical arm in the next detection period is calculated, and the movement of the mechanical arm is controlled based on the angle value of each joint, so that the singular point of the shoulder and / or the singular point of the wrist of the mechanical arm is avoided.

The application discloses a lower limb action classification and multi-joint angle prediction method and system based on multi-modal perception, and comprises the following steps: acquiring hip surface electromyography signals and knee flexible bending signals; using a residual gate channel attention mechanism to adaptively recalibrate each channel of the two types of signals; generating a gate coefficient for each channel, limiting the value range to a preset interval with 1 as the center, multiplying the gate coefficient with the input signal to obtain a recalibrated signal; inputting the recalibrated signals into first and second time sequence feature encoders respectively, extracting electromyography time sequence features and flexible bending time sequence features; fusing the two types of features to obtain shared lower limb motion representation, and then inputting the shared lower limb motion representation into an action classification head and a joint angle regression head respectively, and outputting lower limb action categories and continuous hip and knee joint angles. Compared with the prior art, the application fuses hip electromyography signals and knee bending signals, adopts residual gate attention and double-branch coding, and simultaneously realizes lower limb action classification and multi-joint continuous angle prediction.

The application relates to a hand joint angle estimation method, which comprises the following steps: acquiring surface electromyography data and hand joint angle data; processing the acquired surface electromyography data and hand joint angle data; generating training set samples and test set samples according to the processed surface electromyography data and joint angle data; training a prediction model by using the generated training set samples to estimate continuous finger movement; saving the trained optimal prediction model, inputting the test set samples, and performing regression performance evaluation. The application can overcome the long-range dependence problem, and improve the joint angle estimation accuracy and operation speed.

The application discloses a human walking lower limb muscle force solving device and method based on a generative adversarial network and reinforcement learning, which comprises the following steps: introducing model joint angles and human walking joint angles into a generative adversarial network for training, and outputting part of a reward function required by reinforcement learning from the generative adversarial network; obtaining part of a reward function related to human motion effects through kinematics data and dynamics data generated by a muscle-skeleton model; obtaining part of a reward function related to muscle coordination by analyzing surface electromyogram signals and dynamics data obtained by the muscle-skeleton model; and introducing the three parts of reward functions into a policy network for training, so as to obtain muscle force calculation results capable of reflecting muscle coordination. The application can calculate muscle forces of main lower limb muscles in the walking process of different individuals, and provides a theoretical basis for understanding of human motion rules, motion rehabilitation and development of medical equipment, control of humanoid robots and the like.

The present application provides a positioning method, system for a surgical robot, a surgical robot and a storage medium. Present first adjustment data of the joint angle corresponding to each joint of a surgical robot is determined according to present first detected data of the joint bending angle and present first estimated data of the joint bending angle of each joint. An orientation parameter and a movement parameter of the tip of an operating arm is obtained according to the first adjustment data of the joint angle and the length of the operating arm. In this way, the adjustment data of the joint angle can be determined by combining the detected data and estimated data of the joint bending angle, and the detected data of the joint bending angle can be calibrated to ensure the accuracy of the positioning of the surgical robot and the movement of the operating arm.

The application relates to the technical field of data processing, in particular to a multi-dimensional interactive control system based on digital human demonstration, which comprises a state acquisition module, a space mapping relationship between bone node displacement and joint angle values is calculated, displacement data of each bone node is connected with corresponding joint angle values, and a multi-dimensional state vector is generated; each bone node displacement data in the multi-dimensional state vector is operated, and an action feature mapping graph is generated. In the application, the space mapping relationship between bone node displacement and joint angle values is calculated, a multi-dimensional state vector is generated, and an action feature mapping graph is constructed, so that the collection and expression of human action data can be realized; each bone node displacement data in the action feature mapping graph is subjected to block division and address allocation in a memory space, and a dynamic resource allocation table is generated by executing memory fragment arrangement, so that the system operation efficiency and resource utilization rate are improved.

The application belongs to the technical field of motion capture, and discloses a knee joint angle prediction device and method based on physical topology characteristics. The method comprises the following steps: collecting knee joint measurement data comprising muscle electrical signals and inertial sensing signals, performing filtering and normalization preprocessing on the knee joint measurement data to obtain normalized knee joint measurement data; performing feature extraction and feature screening on the normalized knee joint measurement data to obtain knee joint feature data; converting the knee joint feature data into graph data comprising nodes and edges; inputting the graph data into a graph neural network to output a predicted knee joint angle. The device relies on a leg ring and comprises a data acquisition device, a data preprocessing unit, a data feature processing unit, a graph data processing unit and a graph neural network unit. The knee joint angle prediction method improves the prediction accuracy of the knee joint angle. The device has the advantages of simple installation and small interference to human motion, and can accurately predict the knee joint angle.

This invention relates to the field of data processing technology, and more particularly to a method, device, and storage medium for joint motion monitoring based on data analysis. The method includes: identifying gait symmetry features based on hip and knee joint angles, and extracting joint angle features within a gait window; extracting muscle fatigue features based on surface electromyography (EMG) signals; determining device interaction anomaly features based on joint drive currents; determining a comprehensive motion posture index based on gait symmetry and gait stability features; determining a comprehensive exercise load index based on muscle fatigue and device interaction anomaly features, and combining the comprehensive motion posture index to determine joint motion health, and issuing an early warning to the user. This invention integrates multimodal sensor data to comprehensively assess joint motion health from three dimensions: posture, fatigue, and interaction, and achieves intelligent early warning.

This invention discloses a post-orthopedic rehabilitation training monitoring device and system based on intelligent sensing, belonging to the technical field of rehabilitation training monitoring systems. By setting up a data acquisition unit and a data processing unit, it enables the continuous acquisition and unified recording of various types of motion data generated during post-orthopedic rehabilitation training, such as joint angles, muscle activity states, and movement speeds. Real-time monitoring of the patient's training movements is achieved using accelerometers, gyroscopes, and electromyography (EMG) sensors, allowing for the synchronous acquisition of joint movement trajectories, muscle contraction states, and movement rhythms. This enables the system to continuously monitor changes in movement during patient training, providing a complete data foundation for subsequent movement evaluation. Noise removal and outlier processing of the raw data filter out data that may be affected by environmental interference or transient fluctuations during sensor acquisition, thereby reducing the impact of data fluctuations on subsequent analysis.

The present application relates to a kind of three degrees of freedom dexterous hand finger compliance control method, comprising the following steps: establishing three degrees of freedom finger structure model;Passive joint constraint model and finger end kinematics model are constructed;In cartesian space, the compliance control model is constructed, and the end desired position offset is generated;Compliance control model is discretized;Lightweight kinematics inverse solution strategy is used, and only two active joints of third phalanx, second phalanx are solved, and the end desired position offset is mapped into the expected angle of active joint;According to the expected angle of active joint, motor control instruction is generated, third phalanx, second phalanx are driven to move, and first phalanx is driven to move by mechanical linkage, and end compliance interaction is realized;Contact force acquisition, compliance calculation, kinematics inverse solution and joint control step are cyclically executed, and real-time closed-loop control is completed.The method realizes the accurate mapping of end force, end position and active joint angle, and has good adaptability, real-time and compliance.

The application discloses a kind of upper limb rehabilitation auxiliary system and motion control method based on flexible exoskeleton.The system includes flexible exoskeleton body, visual acquisition device unit;The motion control unit is based on the image of upper limb collected by visual acquisition device, and the current joint angle of wearer is solved by human posture recognition algorithm, and the current joint angle is calculated with the target joint angle in the preset rehabilitation training track library, and the basic driving torque is generated by PID control law.The motion control method is superimposed on the basic driving torque to keep force and buffer force two compensation items to overcome the leakage problem of Bowden line flexible transmission in the maintenance stage and the falling stage.The flexible exoskeleton body adopts backpack type driving unit and Bowden line flexible transmission assembly, and the four Bowden lines on the wrist are distributed in four quadrants on the wrist circumference, and the coordinated driving of two degrees of freedom of wrist flexion and ulna / radial deviation is realized by two motors in diagonal pairing according to the preset coupling matrix.The application uses visual feedback to replace the traditional IMU scheme, and realizes the smooth control of upper limb training or power action through torque compensation, coupled driving and safety limit.

The application relates to a mechanical arm inverse kinematics optimization method, a surgical robot and a computer readable medium, wherein the method forms an operation triangle by actively controlling the mechanical arm. If the elbow angle of an elbow joint is kept unchanged, and if the target shoulder deflection angle of a shoulder joint is less than a limit threshold when solving the target end position and posture, it is indicated that the target shoulder deflection angle is within the movable range of the shoulder joint, the elbow angle of the elbow joint can be kept unchanged, the joint angle of a rotating joint contained in the shoulder joint is solved according to the target shoulder deflection angle of the shoulder joint and a target rotation angle of the shoulder joint, and the joint angles of all rotating joints are obtained. Since the elbow angle of the elbow joint is kept unchanged in the control process, the joint angle of the rotating joint contained in the elbow joint is correspondingly unchanged, and it is not necessary to repeatedly solve the inverse solution, so that the solving process of the target joint angle is simplified. Moreover, the scheme of the application can control the geometric shape of the mechanical arm in the process of solving the inverse solution, and the redundancy characteristics of the redundant mechanical arm are fully utilized.

The application provides a lower limb multi-joint angle prediction method, system, device and medium, the method comprises the following steps: collecting multi-modal data of a user, and preprocessing the multi-modal data to obtain a data set; the data set is divided into a training set and a test set, and the training set is embedded to obtain an embedded training set; using the embedded training set, a preset structure double tower model is trained to obtain a lower limb multi-joint angle prediction model; using the test set, the prediction result of the lower limb multi-joint angle prediction model is evaluated to obtain an evaluation result. The application provides a lower limb multi-joint angle prediction method based on a double tower Transformer and multi-modal data to solve the problem that the current lower limb joint angle prediction technology based on multi-modal data ignores muscle and joint coordination, and builds space-time features in the double tower Transformer architecture to improve the prediction performance of the model.

This invention relates to the field of medical rehabilitation engineering technology and discloses an intelligent orthopedic rehabilitation training adaptive adjustment method and system, including an adaptive adjustment system. The adaptive adjustment system includes a rehabilitation movement acquisition unit, a cloud-based intelligent management platform, and a medical consultation interaction unit. By incorporating the adaptive adjustment system, this invention facilitates the objective and comprehensive monitoring of the rehabilitation training process: by simultaneously acquiring motion state data Q1 and biomechanical data Q2 through multimodal devices such as wearable inertial sensors and surface electromyography sensors, the subjective "whether the movement is standard" is transformed into objective time-series data such as joint angle We, trajectory Wr, and electromyography (EMG). This achieves digital recording and quantitative evaluation of the entire training process, realizing a leap from static image evaluation to dynamic recovery process tracking. Combined with the comprehensive analysis of training load state Sl, abnormal trends can be identified earlier than traditional re-examinations.