714 results about "Motion capture" patented technology

The invention discloses an inspection robot collaborative planning method and system based on digital twinning, and the method comprises the steps: constructing a virtual simulation environment of a large-scale component through a point cloud modeling technology, and integrating a motion capture system to achieve the real-time mapping of a physical environment and a virtual model; defining a cooperative mechanism of the scanning robot and the positioning robot, providing global coordinates and path guidance by the positioning robot, and correcting position errors in real time by the motion capture system; simulating a dual-machine motion track in the digital twin model, and identifying a potential collision risk through a conflict prediction model; designing a collaborative planning algorithm under coupling constraint, and performing optimization by taking path optimization and scanning visual angle coverage as a joint target; based on a twin intelligent framework with a body, the path and the visual angle of the robot are dynamically adjusted by combining reinforcement learning with real-time feedback, and self-adaptive collaborative planning is achieved. Through double-machine division cooperation and real-time correction, the scanning efficiency and precision of the large-size component can be remarkably improved, and the collision risk is reduced.

The invention provides an immersive concert experience system based on virtual reality, and belongs to the technical field of virtual reality, and the system comprises a dynamic adjustment module which is used for predicting the preference of a user for stage arrangement, light effects and music types through a machine learning model according to the historical preference data of the user, and outputting the preference to the stage arrangement, light effects and music types; dynamically adjusting visual elements and sound field parameters of the virtual scene and a refresh rate and a field angle of the virtual scene in combination with the user physiological indexes and the user head motion trail which are acquired in real time; the action capture and feedback module is used for capturing an interaction instruction sent to the dynamically adjusted virtual scene by the limb action of the user in real time; and the interaction feedback module is used for enabling the virtual performer to respond to the interaction instruction of the user in real time based on the expression recognition and voice synthesis technology, and generating personalized interaction feedback content. A user constructs an immersive music interaction field domain, and experience logic of a virtual music meeting is completely reconstructed.

The invention relates to a motion capture and evaluation method and device, equipment and a medium. The method comprises the following steps: acquiring motion capture data; repairing the motion capture data to obtain standardized motion data; obtaining a virtual character animation file based on the standardized action data and a virtual character skeleton model; performing multi-dimensional evaluation on the virtual character animation file to generate a comprehensive evaluation report; and adjusting the virtual character animation file based on the comprehensive evaluation report to obtain a final animation file. By adopting the method, the motion capture animation can be automatically evaluated and corrected, and the effect of improving the working efficiency is achieved.

The invention relates to the technical field of image recognition, in particular to an AI-based park intrusion detection and response system, which comprises a target trajectory acquisition module, an event density analysis module, a disturbance behavior recognition module and a response joint control execution module. According to the invention, through fusion of the spatial-temporal characteristics of the target trajectory and motion vector pattern recognition, intrusion behavior dynamic risk assessment and early warning collaborative optimization is realized, and through combination of multi-dimensional data association calculation and event density increment monitoring, abnormal situation sensing sensitivity and discrimination accuracy in a complex scene are improved. Motion vector time sequence modeling and disturbance feature extraction are utilized, the capacity of capturing camouflage behaviors and irregular motion is enhanced, an environment perception and response execution closed-loop control link is constructed by means of intelligent matching and parameter self-adaptive adjustment of multi-source sensing equipment, the dependency degree of manual review is remarkably reduced, the delay from recognition to treatment response is shortened, and the recognition efficiency is improved. And an early warning research and judgment and linkage disposal full-process automation system is formed.

The invention provides a training and examination robot system based on a multi-modal large model, equipment and a computer program product, and aims to accelerate training transformation and upgrading and improve coping ability as basic appeals, and based on regular training work of teams, training and examination robot, sensor acquisition equipment and big data platform three-terminal equipment. A multi-modal data acquisition layer for realizing real-time attitude detection and motion capture by using an edge calculation algorithm, a data processing and analysis layer for finishing real-time image processing through localized edge calculation in a network-free or power-free environment, and system control functions for finishing intelligent decision making, evaluation, error correction and the like are provided; an intelligent decision-making and control layer of a training subject rewriting decision-making mechanism is established, cross-modal collaborative learning middleware is utilized to realize modal representation consistency while training subject transposition evaluation is realized, training and assessment operations of the same subject of multiple persons or different subjects of multiple persons are supported, and the training and assessment efficiency is remarkably improved.

The invention discloses a sign language recognition method and device based on multi-modal deep learning, and the method comprises the steps: multi-modal data input: capturing hand motions, gesture tracks and facial expressions at the same time through a camera, a motion capture sensor and other devices, and forming multi-modal data input; sign language action recognition: precise recognition of sign language actions is realized through a combined model of a deep convolutional neural network and a long-short-term memory network; facial expression and gesture track combined recognition: realizing understanding and translation of complex sign language sentences by combining the captured facial expressions and gesture tracks; and context natural language processing: generating a target statement in combination with context semantic understanding, and outputting a translation result. Through hand motion capture, facial expression analysis, gesture trajectory tracking and context natural language processing, complex sign language motions can be recognized more accurately and translated into characters or voices in real time, and low delay and high accuracy are achieved.

The invention relates to an all-humanoid robot motion redirection method and device based on motion capture equipment, and the method comprises the steps: converting a human body posture quaternion collected by the motion capture equipment into a rotation matrix, and achieving the precise mapping from a Cartesian space to a robot joint space in combination with an Euler angle decomposition strategy; a quaternion-disassembled joint angle is used as an initial solution, various physical constraints are introduced in real time according to the actual supporting state of the robot, and the lower limb joint angle is corrected online by constructing and solving an inverse kinematics problem with various constraints, so that the robot reproduces the action of an operator, and the joint angle of the lower limb can be corrected online. And the requirements of joint limiting, self-collision avoidance, dynamic balance and the like are strictly met, so that the accuracy of motion simulation and the stability of the system are effectively unified.

The invention relates to a multi-source perception-based mechanical arm operation data acquisition system and method, the system comprises an isomorphic mechanical arm operation module, a multi-view visual image acquisition module, an optical motion capture module and a shielding detection and data fusion module, vision, pose and force sense data are cooperatively acquired through multiple sensors, and time-space consistency is synchronously kept by adopting hardware. The system has a multi-dimensional occlusion detection mechanism and can dynamically switch data processing strategies according to the occlusion degree: visual data is preferentially adopted when no occlusion exists, multi-sensor information is fused when partial occlusion exists, and force feedback compensation is predicted and introduced based on a dynamic model when complete occlusion exists. Physical feedback and visual perception are creatively combined, the problem of data missing of a traditional single sensor data acquisition system under the shielding condition is solved, more comprehensive and more reliable data support is provided for mechanical arm algorithm training and performance optimization, and the method is particularly suitable for complex operation scenes with shielding.

There is provided an information processing device, an information processing method, and a program that improve detection accuracy in detecting a motion or posture of a person or an object using an inertial sensor. A posture of a measurement target is estimated based on a position and orientation of each of a plurality of measurement devices arranged at a plurality of parts of the measurement target and based on a connection relationship between the measurement devices that is set based on positions and orientations of the measurement devices, each measurement device including an inertial sensor. The present technology can be applied to a motion capture system.

The invention relates to the technical field of medical hygiene, and particularly discloses a hygiene visual intelligent monitoring and guiding system suitable for surgical hand disinfection, hand motion data is collected by fusing a multi-modal motion capture module with a depth camera and an inertial sensor, and a high-risk area identification module analyzes the risk of microbial residues in finger seams and fingernail edges. The three-dimensional motion modeling module constructs a biomechanical model to calculate a trajectory, the dynamic compliance guiding module marks deviation in real time through optical projection, the multi-source data fusion module integrates a risk value and a spectral data evaluation effect, and the intelligent decision engine module outputs an operation matching degree and a correction instruction based on a neural network. Omnibearing monitoring, accurate risk assessment, real-time action guidance and quantitative effect evaluation of the hand disinfection process of an operator are realized, the standardization and effectiveness of surgical hand disinfection operation are finally improved, and the infection risk of a surgical site is reduced.

The invention belongs to the technical field of man-machine interaction and robot control, and particularly relates to a man-machine physical twin system based on multi-modal video analysis and adaptive mapping and a control method, and the system comprises a multi-modal collection unit which is used for collecting original video streams of human body actions, expressions and voices, and compensating and repairing dynamic shielding; the semantic analysis unit comprises an action analysis module used for extracting a human skeleton key point set object interaction track from the original video stream, and an expression / voice analysis module used for outputting a facial action unit coefficient and a voice text with an emotion label; the self-adaptive mapping unit is used for establishing human body-robot joint kinematics mapping, distributing control bandwidth in real time according to task types and degrading non-key modes based on network states to guarantee action continuity, and non-contact action capture is achieved through a pure vision scheme by eliminating dependence on a wearable sensor; and the problems of failure and the like of a traditional visual scheme under shielding and illumination variation are solved.

The invention relates to a robot teleoperation algorithm system based on motion capture equipment. The system comprises a motion capture data receiving module, a pose conversion module, a safety monitoring module and a control instruction issuing module. Teleoperation is carried out by using a mode of converting data of the motion capture equipment into a robot control instruction, the efficiency of completing a specific task by the teleoperation robot can be greatly improved, multiple control modes are realized, and an operator can more intuitively operate the robot to complete the task; meanwhile, the safety monitoring module designed in the system can ensure the safety of operators and the robot when the motion capture data is abnormal or drifts, and has high robustness. Moreover, the system can achieve the adaptation to different robots, and can reduce the adaptation development amount during hardware migration.

The invention relates to the technical field of motion capture, in particular to a running posture continuous capture analysis method for training, which comprises the following steps of: predicting a joint position and speed of a current frame based on a joint state vector of a previous frame and a currently processed single-frame image, acquiring a priori motion posture vector, and acquiring a priori motion posture vector; performing 2D key point positioning on the current single-frame image in parallel, extracting an incomplete joint coordinate measurement value caused by limb swing shielding, and combining the prior motion attitude vector with the joint coordinate measurement value; according to the method, the joint position and speed of the current frame are predicted based on cooperative processing of the previous frame joint state and the current image feature information, continuous and stable prior attitude data are obtained, meanwhile, the missing joint position measurement value influenced by limb shielding in the single frame image is extracted in parallel, and the prediction data and the actual measurement value are fused and compensated. Therefore, the technical problem that joint coordinates are not complete due to shielding is solved, and the continuity and stability of attitude capture are improved.

The invention provides virtual reality-based dance teaching equipment and a teaching method thereof, and the equipment comprises a VR module which is used for providing multi-view immersive visual experience; the motion capture module is used for capturing the joint angle of the dancer in real time and the limb track feedback module and is used for simulating the ground counter-acting force in dancing through vibration or pressure, enhancing motion perception and providing a touch prompt for motion correction; the virtual scene module is used for creating a dance virtual scene; and the action analysis module analyzes the joint action deviation based on multiple view angles, generates an instant score according to the joint angle deviation and the rhythm synchronization rate, and records historical data to generate a progress curve. According to the method, the problems of resource shortage, feedback lag and other pain points in traditional dance teaching are solved, efficient, personalized and interesting dance learning experience is achieved through multi-modal interaction and a dynamic adjustment mechanism, and the application of the method is expected to promote dance education to develop in the intelligent and universal direction.

The invention discloses a test verification platform and a test verification method for a motion compensation gangway ladder of an offshore wind power operation and maintenance ship. The platform comprises a system calculation and control center, an environment simulation vibration platform, an active motion compensation execution mechanism, a high-precision sensing and sensing module, a multi-channel data acquisition and storage system, an offshore wind turbine target platform module and an optical motion capture module. The platform reproduces six-degree-of-freedom motion of the operation and maintenance ship through integrated cooperation and high fidelity, simulates active compensation of a gangway ladder and dynamic characteristics of a hydraulic system, and accurately measures key parameters such as poses, forces / torques and the like by using multiple sensors and an optical capture technology. The method comprises the steps of motion instruction synthesis, platform motion reproduction, compensation action simulation, data synchronous acquisition and performance evaluation. According to the invention, high-precision land simulation verification of ship-gangway ladder-fan dynamic coupling is realized, the problems of high cost, high risk and poor repeatability of offshore actual measurement are effectively solved, and a reliable and efficient solution is provided for performance verification of a motion compensation system.

The invention relates to the technical field of man-machine collaboration and information processing, discloses an AI processing-based motion capture recognition pre-judgment system, and aims to solve the problems of difficulty in physiological signal monitoring and performance drifting of a myoelectricity intention recognition model in a high-noise environment, and the AI processing-based motion capture recognition pre-judgment system comprises a processing module, a visual capture module, a myoelectricity acquisition module, a bioacoustics acquisition module and an information prompting module, according to the method, myoelectricity and visual information decoding operation intentions are fused, a self-adaptive filter is guided through the intentions, accurate noise suppression is carried out on biological acoustic signals of a target object, and therefore the physiological state risk of the target object is accurately evaluated, meanwhile, an online self-adaptive calibration mechanism is introduced, the actual action of visual recognition serves as a supervision true value, and the accuracy of the physiological state risk of the target object is improved. And the myoelectricity model is continuously calibrated, so that the accuracy of long-term identification is ensured. According to the method, the decoding intention and the physiological risk level are integrated, the composite cooperation instruction is intelligently generated and fed back to the operator, the situation awareness ability of the operator is enhanced, and the safety and efficiency of man-machine cooperation are improved.

The invention discloses a virtual reality interactive simulation system based on high-speed rail line maintenance, and belongs to the technical field of high-speed rail line maintenance simulation. The system comprises a virtual scene construction unit, an operation action capture unit, a rule verification unit, a difference calculation unit, an abnormity positioning unit and a comprehensive risk prompting unit. The virtual scene construction unit generates a standardized virtual scene model by using high-speed rail line maintenance manual data and real-time equipment state information; the operation motion capture unit captures a user three-dimensional space coordinate sequence and outputs motion track data; the rule verification unit compares the action track data with a preset maintenance rule base and outputs an operation compliance judgment result; the difference calculation unit calculates an operation deviation measure; an abnormal positioning unit positions an abnormal operation physical position; and the comprehensive risk prompting unit generates a maintenance risk grade identifier. The system provides a simulation environment for maintenance personnel, and carries out real-time monitoring and feedback operation.

The invention relates to the technical field of attention deficit hyperactivity disorder detection, and discloses a device for detecting cognitive and developmental conditions of children with hyperactivity disorder, which comprises a handle, a head-mounted device, a bracelet, a hierarchical adaptive algorithm module, a multi-dimensional execution module and a data fusion and analysis module, a multi-mode sensing module is arranged in each of the handle, the head-mounted device and the bracelet; a motion capture unit, a high-sensitivity key array, a tactile feedback module and a wireless transmission module are integrated in the handle, and a wireless electroencephalogram acquisition module, a miniature near infrared spectrum module and an eye movement tracking module are integrated in the head-mounted device. According to the invention, through deep fusion of a multi-dimensional cognitive evaluation system and gamepad hardware, the device not only breaks through the limitation of the traditional neuropsychological test in methodology, but also realizes refined capture of ADHD heterogeneity characteristics in the technical level, and provides a hardware basis for establishing an objective and quantitative diagnostic tool with high ecological efficiency.

This disclosure pertains to a sports training system that utilizes deep learning and computer vision technologies to analyze biomechanical sport motions. The system is equipped with AI-powered functionalities to enhance training, including those related to creating and managing motion templates that integrate video sequences with extracted motion features. These templates serve as benchmarks, accessible to trainees for improving their technique and performance. The system also can employ an advanced motion synchronization process, which combines dynamic time warping and computer vision technologies to align trainee videos with reference motions captured in motion templates. Additionally, a motion feature database can be updated with features extracted from both trainee and trainer videos, storing visual information in a structured format to facilitate various analytic functions. The system supports analysis of a wide range of sports motions.

The invention relates to the technical field of posture recognition, in particular to a motion posture recognition method and system based on deep learning, and the method comprises the following steps: based on a human body motion image sequence, analyzing a skeleton center space trajectory, adjusting an acquisition visual angle, recognizing staged skeleton nodes, and generating a node time sequence trigger sequence; and optimizing joint point angle feature grouping and expression to obtain a hierarchical feature expression structure. In the invention, through detection for interference dynamic change characteristics, visual angle adjustment and node staged response in a time sequence are combined, compensation reasoning of a space trajectory is enhanced, and the hierarchical expression capability of motion capture is improved; the input features have the advantages of actively screening background disturbance, dynamically adapting the motion direction, separating action core nodes and perfecting node response association and grouping angle time sequence structures, the integrity and identification degree of action feature structure expression and hierarchical modeling are guaranteed, and higher identification accuracy and complex environment adaptability are brought.

The invention discloses a motion capture method based on sports equipment training, and relates to the technical field of intelligent motion capture, and the method comprises the steps: extracting a skeleton key point set of a detected object from a training ground image data stream through employing a human body key point detection algorithm, and extracting a two-dimensional contour region of sports equipment through employing a target segmentation algorithm, meanwhile, recording time index information of the corresponding image frames, performing synchronous comparison by utilizing timestamps of the training event data streams, establishing a time sequence mapping relation, and obtaining time sequence action data; taking the time sequence action data as a space anchor point, carrying out middle frame reconstruction on the high-speed motion stage based on a time sequence mapping relation, recovering an equipment motion track, and carrying out joint matching to generate a time sequence fusion sequence; establishing a multi-view geometric constraint through a time sequence fusion sequence and camera pose parameter information, and recovering a human body three-dimensional skeleton point cloud and an initial six-dimensional pose of the equipment; the space-time consistency and reconstruction precision of motion capture in a high-speed dynamic training scene are effectively improved.

The invention discloses an instrument of an intelligent breathing and motion capture system based on six-character table health maintenance, and relates to the technical field of intelligent health maintenance equipment, the instrument comprises a hardware part and a software part; the hardware part comprises a mouth shape visual capture assembly, an action force and angle sensing assembly, an audio acquisition and feedback assembly and a main control and power supply assembly; the software part comprises a data acquisition module, a signal preprocessing module, a parameter analysis and judgment module, a real-time feedback module and a data storage and management module; according to the invention, through integration of the mouth-shaped visual capture assembly, the action force and angle sensing assembly, the audio acquisition and feedback assembly and the master control and power supply assembly, synchronous acquisition and processing of multi-source data are realized; the mouth shape change, the body movement and the audio pronunciation of the user can be accurately captured in real time, and powerful technical support is provided for standardized practice of six-character table health maintenance.

The invention provides an elevator guide rail digital installation method based on a laser tracker and a motion capture system, which comprises the following steps that: A1, a shaft global coordinate system is constructed, a shaft local measurement network is formed, and a lifting operation platform is provided with a guide structure matched with an installed guide rail; a2, after multiple sets of target ball space coordinate data are collected, the conversion relation between a motion capture camera array coordinate system and a laser tracker coordinate system is calculated through an optimization method based on rigid body distance constraint; a3, through a coordinate transformation and projection correction algorithm, the installation position data of the elevator guide rail support is converted into a two-dimensional projection image, and the two-dimensional projection image is projected to the well wall face through projection equipment; a4, acquiring three-dimensional coordinate data of the installed guide rail reflection marker through the motion capture camera array, and calculating a deviation matrix of an actual installation position and the design model so as to correct the installation deviation of the guide rail; and A5, the steps A2 to A4 are executed again to install the next section of guide rail, and the benchmark accumulative error is eliminated through recalibration.

The present invention relates to a method for creating an individualized machine learning inference model. In accordance with the invention, the aforementioned method and a related system involve a motion capture device adapted to be worn by a user. The motion capture device is adapted to acquire measurements, which are used to compute a first estimate of one or more gait parameters. Next, a database is accessed that contains previously collected observations of gait data and the first estimate is compared to the previously collected observations of gait data. Finally, the subset of previously collected observations of gait data that is most informative for the particular user is identified and the individualized machine learning inference model can be developed using the identified subset of previously collected observations of gait data.

The invention discloses a digital stable jaw pad designing and manufacturing method, and relates to the technical field of oral rehabilitation and digital medical technology.The digital stable jaw pad manufacturing method comprises the following steps that a, three-dimensional point cloud data of oral dentition and mucosa of a patient is obtained through a three-dimensional scanner; b) determining a stable jaw position coordinate model by using an optical motion capture system; c) designing a jaw pad three-dimensional entity model with an anterior tooth area, a posterior tooth area and an edge structure based on the three-dimensional point cloud data and the stable jaw coordinate model; and d) manufacturing the jaw pad through 3D printing equipment, wherein the 3D printing equipment is formed by adopting a polyether-ether-ketone material. By integrating digital acquisition, design and manufacturing technologies, the pain point of a traditional method is solved, and personalized, high-precision and rapid manufacturing of the stable jaw pad is realized.

The invention relates to a double-robot automatic calibration method based on self-adaptive point location planning in a restricted space, and belongs to the technical field of double-robot calibration, and the method comprises the steps: building a three-dimensional simulation model of a double-robot operation space on an Rviz platform based on an ROS architecture; a collision detection module is used for randomly generating a calibration point composed of collision-free double-robot joint angle vectors; when the robot is driven to move, data of the motion capture camera and positive kinematics Cartesian coordinates of the double robots are synchronously collected, a calibration result is obtained through optimization of a least square method by adopting a hand-eye calibration algorithm, and the effectiveness of the calibration result is judged through the average value of data calibration precision until calibration of the double robots is completed. Simulation modeling, collision detection and a hand-eye calibration technology are fused, a complex calibration problem is converted into a virtual environment parameter optimization process through a self-adaptive sampling strategy and a data synchronous acquisition mechanism, calibration robustness and precision are remarkably improved, and guarantee is provided for high-precision cooperative control of double robots.

The invention discloses a self-service vision detector for AI human body action reading, which comprises an action capture module, an eye movement tracking module, a self-adaptive test module, a feedback and suggestion module, an intelligent diagnosis module and a user interaction interface module, and is characterized in that the action capture module captures and analyzes human body actions in real time by using a camera or a sensor; the eye movement tracking module detects visual response of a user by analyzing movement tracks of eyeballs in real time, the self-adaptive testing module automatically adjusts difficulty of testing content according to eye movement data of the user, and the feedback and suggestion module analyzes results according to response time of the user and focusing precision data of the eyes. According to the method, the vision level of the user is automatically judged, a detailed diagnosis report is generated, and compared with the prior art, a feedback mechanism can reduce man-made interference and improve the accuracy and reliability of testing.

The invention discloses an intelligent household electrical appliance human engineering design optimization method and device based on a large model, and the method comprises the steps: collecting user motion and interaction data through a camera, a motion capture sensor and other devices, and extracting joint motion, operation frequency and other ergonomic features after time synchronization, coordinate calibration and noise cleaning; secondly, constructing a knowledge graph fusing anthropometry and biomechanics knowledge, performing graph embedding through graph convolutional network coding, and performing attention mechanism fusion with feature vectors; and then, training is carried out based on a large model of a Transform architecture in combination with ergonomic scoring constraints, and home appliance design parameters are optimized by using an evolutionary algorithm to generate a personalized scheme adapted to the height and operation habits of the user. According to the method, through cooperation of multi-modal data driving and domain knowledge, the comfort, accessibility and user adaptation precision of the intelligent household electrical appliance are remarkably improved.

The invention relates to a basketball player motion capture analysis method and system based on an industrial camera. The method comprises the following steps: based on a preset three-dimensional human body model, obtaining a three-dimensional human body surface grid sequence and internal skeleton joint kinematics data by combining a pixel-level mask of each multi-view image frame set; detecting a body contact event and a continuous time period based on the Euclidean distance of the three-dimensional human body surface grid sequence; based on the internal skeleton joint kinematics data, calculating a multi-dimensional kinematics feature time sequence; constructing a dynamic causal hypothesis based on the body contact event, checking the dynamic causal hypothesis based on a preset differentiable forward dynamic multi-rigid-body human body model and a multi-dimensional kinematics characteristic time sequence to obtain a loss function value, and judging the hypothesis with the smaller loss function value as a real dynamic causal relationship. And the identity identification information is combined to obtain the identity of the active force generation party and the identity of the passive force bearing party. By adopting the method, a real force exerting main body and a real force bearing main body in confrontation can be accurately identified.

The invention relates to a human body pose tracking method. The method comprises the following steps: establishing a world coordinate system by using an SLAM (Simultaneous Localization and Mapping) system of head-mounted equipment, and acquiring an absolute pose of the head of a user in the world coordinate system; creating an individualized motion model, and obtaining individualized motion model parameters; acquiring real-time pose data of a distributed IMU node, wherein the distributed IMU node comprises a plurality of IMU units distributed on user limbs; and creating a factor graph optimization model, and determining the human body pose of the user by taking the absolute pose of the head as a root node and combining the individualized motion model parameters and the real-time pose data. The method is high in tracking precision, and can avoid the reduction of the tracking precision of the human body pose caused by integral drift. The invention further provides a human body pose tracking device, a robot control method and motion capture gloves.