19 results about "Inertial motion capture" patented technology

The invention provides an industrial humanoid robot teleoperation cooperation system and method based on multi-modal motion capture fusion, and belongs to the technical field of industrial humanoid robots. According to the invention, an optical motion measurement module is used for collecting whole body position data of an operator in an unshielded scene; the inertial motion capture module is used for collecting joint angle data of an operator in a shielding scene; the mode switching module is used for switching working modes; the environment sensing module is used for collecting environment data, workpiece images and robot tail end contact force information and sending the information to the cooperative control unit. The force feedback device is used for converting the contact force information of the tail end of the robot into tactile vibration and collecting grip strength data of an operator; the cooperative control unit is used for fusing the multi-modal dynamic capture pose data and generating an execution instruction in combination with an obstacle avoidance algorithm and a force control model; the industrial humanoid robot is used for receiving the execution instruction. The operation stability and environmental adaptability of the robot in scenes such as high-precision assembly and heavy carrying can be improved.

This invention belongs to the field of deep learning, specifically relating to a posture-induced multimodal gait recognition method aimed at improving the accuracy of gait recognition in complex environments. The method includes constructing a multimodal dataset containing gait video sequences, corresponding depth map sequences, and motion-captured posture sequences by combining a full-body inertial motion capture device, a binocular depth camera, and an RGB camera. Cross-device gait feature alignment preprocessing is performed on the multimodal dataset to obtain spatiotemporally aligned motion-captured postures and video postures. A 3D posture induction module is constructed, using the spatiotemporally aligned motion-captured postures as a reference, and trained under supervised training via graph feature alignment loss to extract induced posture information from the video postures. A multimodal recognition model fusing induced posture information and gait contour features is constructed to achieve gait recognition.

The application provides an IMU calibration method, device, medium and product for an inertial motion capture system, wherein the inertial motion capture system comprises a plurality of IMUs, a calibration parameter solving network and a pose estimation model, the method comprising: S1, obtaining IMU rotation reading sequences of the plurality of IMUs at a fixed time; S2, calculating rotation richness of each IMU rotation reading sequence; S3, inputting each IMU rotation reading sequence into the calibration parameter solving network, estimating calibration parameters of each IMU through the calibration parameter solving network, and updating the calibration parameters whose rotation richness is higher than a predetermined threshold; S4, calculating the updated calibration parameters and the IMU rotation reading sequences corresponding to the updated calibration parameters to obtain global joint poses, and inputting the global joint poses into the pose estimation model. By using the above technical scheme, the accuracy of inertial motion capture is improved, the user experience is optimized, and the inertial motion capture system remains efficient and stable in long-time continuous use.

The application discloses a kind of human motion digital twin construction methods based on inertial action capture technology.Firstly, action capture device is worn on human body, and the original sensing data of each joint corresponding sensor is collected, the original sensing data is processed using adaptive extended Kalman filter algorithm, the attitude of each joint corresponding sensor is obtained, and the original sensing data and sensor attitude data are sent to host computer;Host computer constructs human motion digital twin model according to human skeleton;The attitude calibration matrix of each joint is calculated using human posture calibration method, and the real-time sensor attitude data is calibrated to obtain the attitude of each joint of human body, drive human motion digital twin model;While driving the model, the joint parameters calculated are compared with human joint parameter threshold, and the evolution model is dynamically updated.The application can realize that human motion digital twin model is synchronously mapped real human motion, and model is updated in real time dynamically.

[Problem] To accurately correct positional errors in inertial motion capture. [Solution] Provided is an information processing system comprising: an acquisition unit that acquires inertial data obtained by each of a plurality of inertial motion capture devices attached respectively to a plurality of sites on a user, including a reference site, and position measurement data obtained using a wireless tag attached to the reference site and synchronized with the inertial data; and a correction unit that corrects, on the basis of the position measurement data, the pose of the reference site estimated on the basis of the inertial data, wherein the correction unit estimates the poses of a plurality of joints, including the reference site, on the basis of the corrected pose of the reference site.

The invention discloses a physical education teaching quality dynamic evaluation system and method based on artificial intelligence, and the system and method are realized through the following steps: employing an inertial motion capture sensor, a non-embedded physiological signal measurement device, a camera and a microphone; collecting motion postures and physiological data of students and teaching behaviors and explanation contents of teachers in real time; after cleaning, denoising and format conversion are carried out on the collected data, feature extraction and mode recognition are carried out by adopting a machine learning and deep learning model, and the exercise performance of students and the teaching effect of teachers are analyzed; a dynamic evaluation model is constructed based on the analysis result, and the model integrates a motion efficiency comprehensive evaluation function and comprehensively considers the motion accuracy, the motion stability, the physiological load index and the motion sequence fractal dimension to achieve objective evaluation; finally, personalized teaching suggestions are provided for teachers according to evaluation results, and customized learning paths and resources are planned for students. According to the invention, real-time, dynamic and intelligent assessment of the physical education teaching process is realized, the assessment efficiency and accuracy are significantly improved, and teaching optimization and personalized development are promoted.

The application discloses a motion sensing jacket and a motion sensing system, wherein the motion sensing jacket comprises a jacket body and a distributed strain sensor; the jacket body is elastic; the distributed strain sensor comprises a stretchable conductor unit; the stretchable conductor unit comprises a plurality of concentrated sensing areas; the plurality of concentrated sensing areas are arranged on at least all or part of the following areas on the jacket body: an area corresponding to the latissimus dorsi muscle, an area corresponding to the trapezius muscle and an area corresponding to the biceps brachii muscle; and the concentrated sensing area is flexible so that the concentrated sensing area can generate an electrical property change following deformation of the jacket body. In the scheme, the concentrated sensing area is arranged on the area corresponding to the main active muscle on the jacket body, the activity of the corresponding part can be detected through the concentrated sensing area, and compared with optical positioning and motion capture technology and inertial motion capture technology, the concentrated sensing area has the characteristics of high precision, low cost and being not easily disturbed by the outside world.

This application discloses a motion data redirection method, system, device, and storage medium. The method includes the following steps: acquiring the first skeleton coordinates of the target character, the second skeleton coordinates of the original character, the first driving quaternion of the original character in the current frame, the first root node coordinate information of the original character in the current frame, the first whole-body node coordinate information of the target character in the previous frame, and the fourth whole-body node coordinate information of the original character in the previous frame; determining the temporary second whole-body node coordinate information of the target character in the current frame; determining the third whole-body node coordinate information of the original character in the current frame; determining the node identifier with the smallest coordinate change; determining the motion change of the node corresponding to the node identifier in the target character; and adjusting the first whole-body node coordinate information of the target character in the current frame. This method can be applied to both real-time and non-real-time motion data redirection, and has strong practicality and scalability. This application can be widely used in the field of inertial motion capture technology.

The invention discloses an unmanned aerial vehicle wind resistance performance test system and method based on a multi-fan array ventilation wall. The system comprises a movable ventilation wall, an auxiliary test assembly and a data acquisition and analysis assembly. The auxiliary test assembly comprises a rainwater spraying module and a closed-loop wind control module, the rainwater spraying module is used for adjusting the rainfall intensity of the unmanned aerial vehicle flight test site, and the closed-loop wind control module is used for adjusting the wind speed of a fan unit in the control module; and the data acquisition and analysis assembly comprises a positioning base station and an inertia motion capture module and is used for acquiring positioning data and flight attitude data of the unmanned aerial vehicle so as to analyze the wind resistance of the unmanned aerial vehicle. The problems of dynamic response lagging and insufficient wind and rain coupling environment simulation capability in the traditional test technology can be effectively solved through precise synchronous control of the multi-fan array ventilation wall combined with the rainwater spraying module and the closed-loop wind control module and comprehensive data acquisition of the positioning base station and the inertia motion capture module.

The application discloses a virtual and real gesture interaction calibration method, comprising the following steps: acquiring a posture quaternion under a first gesture, a first orientation of a virtual object in a virtual scene, and a first angle difference; determining a first calibration quaternion according to the posture quaternion and the first angle difference; performing first calibration on the posture quaternion according to the first calibration quaternion to obtain a first target quaternion; performing second calibration on a virtual model hand according to the first target quaternion after the first calibration and an initial coordinate of a skeleton root node to obtain a first skeleton root node coordinate; determining a second target quaternion of the virtual model hand and a second skeleton root node coordinate according to a second angle difference; and performing third calibration on the virtual model hand according to the second target quaternion and the second skeleton root node coordinate. The method can improve user experience. The application can be widely applied in the technical field of inertial motion capture.

This application relates to the fields of motion capture technology and computer human body model technology, and particularly to a method, device, electronic device, and storage medium for automatic adjustment of a surface model. The method includes: acquiring the posture data of a reference human body at the current moment; adjusting the current posture of the surface model of the reference human body based on the posture data to obtain the target posture of the surface model, and acquiring posture representation data of a model human body using an inertial motion capture system; determining the mapping relationship between the reference human body and the model human body, and adjusting the target posture of the surface model based on the mapping relationship and the posture representation data to obtain a dynamic surface model that changes over time. This solves the problems in related technologies where posture human body model modeling requires manual adjustment, has a low degree of automation, and where automatic adjustment of human body models based on optical motion capture is expensive, complex to operate, and has a narrow range of applications.

The application discloses a real-time inertial motion capture method and system based on position anchor enhancement, collects inertial measurement unit acceleration, rotation signals, limb end position anchors and visibility tags provided by a head-mounted display device, encodes into a multi-modal observation feature vector, acquires human shape parameters and an initial motion state summary to construct a global structured condition vector, generates a frame-by-frame validity mask according to the visibility tag to perform gating processing, inputs the multi-modal feature after the gating processing into a transformer network to output human posture parameters, joint position parameters and motion speed parameters through adaptive layer normalization by using the condition vector, takes visual inertial poses output by the head-mounted display device as a stable head direction reference to perform online dynamic calibration, and reconstructs whole body three-dimensional joint positions and a human mesh model through forward kinematics. The application utilizes spatial position anchors to suppress end error amplification and heading drift, and realizes high-precision, long-time stable wearable motion capture.

A robot learning dataset construction method, a humanoid robot, and a computer-readable storage medium are provided. The method includes: a robot operator uses an inertial motion capturing device to remotely operate a humanoid robot to perform various operation tasks, construct a dataset by collecting real-time working images and real-time joint motion data of the humanoid robot performing various operation tasks under manual guidance, thereby utilizing the flexible combination of the human body posture direct extraction characteristics of the inertial motion capturing device and the flexibility and reliability of executing robot remote operation tasks so as to improve the construction efficiency, dataset validity, and dataset integrity of the task learning dataset, which facilitates further improvement of the generalization and accuracy of the imitation learning of the humanoid robot.

The invention discloses an inertial motion capture method, device and system and a storage medium, and the method comprises the steps: constructing a ToF data simulation model, and obtaining node data measured by an IMU sensor and a ToF sensor in real time; node center data fusion is carried out to obtain node sensing features, and data coding is completed based on Transform Encoder according to the node data; performing dynamic spatial position coding, regarding node positions as functions of motion signals, and performing sine and cosine position coding in the network; adding the dynamic space position codes and the node sensing features and inputting the added codes and the node sensing features into an encoder; after dynamic space position coding and node sensing coding are completed, the fusion feature ZNCI is input into three cascaded LSTM motion estimators, and calculation of the speed, the position and rotation is achieved; carrying out adaptive fusion calculation on the end point motion state according to the three cascaded motion estimators; according to the method, the positioning precision of the tail end of the key node can be improved, and attitude features in nonlinear and slowly-varying actions can be captured more accurately.

This invention relates to the fields of artificial intelligence and sports science, and particularly to a method for evaluating the stability of tennis shots based on inertial motion capture. The method includes constructing a spatiotemporal graph of the skeletal sequence of joints and T-frames, where the spatiotemporal graph represents the three-dimensional spatial information of each joint within a frame and the temporal information of node changes between frames; constructing a motion recognition network based on a graph neural network to identify the shot type; after identifying the user's shot type, randomly selecting two shots of the same type, and using a dynamic time warp algorithm to calculate the optimal path distance between the two shots; grouping the human joint nodes, calculating the stability and weight of each group of joint nodes, and weighting the stability of the group of joint nodes using the weights; converting the obtained weighted stability to a percentage system to obtain the final stability evaluation score; this invention can more accurately identify tennis motions and provide more accurate shot stability evaluation results.

The application discloses a kind of robot whole body control method and system based on real-time inertial motion capture and reinforcement learning, belong to robot control field.The system includes the motion capture module, data processing and forward kinematics module, motion redirection and optimization module, general motion strategy control module and real-time control and driving module in turn link, form end-to-end closed loop control flow line by shared memory and high-speed communication protocol.The method is realized high-quality mapping from human posture to robot model by collecting human joint data through inertial motion capture equipment, pose solution, non-uniform scaling and two-stage differential inverse kinematics optimization, and generates stable joint instructions combined with reinforcement learning strategy trained by PPO algorithm.The application solves the problems of lack of feedback loop, joint deadlock and contradiction between realism and stability in the prior art, has the characteristics of strong robustness and high expansibility, and can be widely applied to remote operation, motion reproduction and other scenes.

The invention provides a human body linkage immersive VR sightseeing interaction system and a human body linkage immersive VR sightseeing interaction method, and the system employs a motion perception-intelligent decision-scene response integrated interaction architecture. Comprising a leg motion sensing module, a motion-scene collaborative decision-making module and an immersive VR park scene presentation module worn on the head, wherein the leg motion sensing module realizes millisecond-level data linkage by adopting BLE 5.0 connection and is fixedly mounted at the middle section of crus gastrocnemius muscle; wherein the leg motion sensing module comprises a six-axis inertial motion capturing unit used for collecting walking motion characteristics of the old people; the exercise-scene collaborative decision-making module comprises an old people exercise ability adaptation unit with a built-in dynamic threshold model; and the immersive VR park scene presentation module comprises a multi-sensory immersion unit with a built-in natural sound effect library. According to the invention, the solitary feeling of old people during exercise is relieved, and the exercise willingness is improved; the elderly are guided to actively adjust the exercise intensity, and safety and the exercise effect are both considered; the device adapts to the physical characteristics and capabilities of old people.

The invention relates to a regional muscle load grade prediction method and device based on deep learning. The method comprises the following steps: acquiring original motion data captured by inertial motion; carrying out standardized pretreatment on the sample; driving an individualized biomechanical simulation model by using the preprocessed data, and generating a high-fidelity muscle load grade label data set through muscle force calculation, regional aggregation and grade discretization; training a deep learning model according to the data set; and finally, using the trained model to quickly predict new motion data, and outputting the load level of each muscle area. According to the method, through an integrated process, the contradiction between label scarcity and real-time requirements in non-intrusive muscle load assessment is solved, and efficient and interpretable muscle load assessment is realized.

This invention relates to a multimodal spatiotemporal alignment and interactive reconstruction method for digital twins of intangible cultural heritage skills, belonging to the interdisciplinary field of digital protection of intangible cultural heritage and computer graphics. It simultaneously acquires five modalities: optical and inertial motion capture, hyperspectral imaging, panoramic sound field, multi-view images, and physiological data of inheritors. Temporal alignment is achieved through cubic spline interpolation and Gaussian mixture model expectation-maximization algorithm, and spatial alignment is completed using point cloud registration. A dynamic graph spatiotemporal interaction network is used to generate cross-modal fusion features. Based on this, a three-level digital twin is constructed: geometric, technological, and knowledge-based. The technological twin extracts temporal evolution features based on Transformer, while the knowledge twin expresses causal transmission relationships using process hyperedges. Finally, interactive experiences are provided through dynamic geometric optimization, multimodal immersive rendering, and gesture / controller interaction, and user behavior feedback is used for incremental learning and knowledge graph updates, forming a self-evolving closed loop.