36 results about "Motor behaviour" patented technology

The invention relates to the technical field of control systems for industrial robots, and more specifically relates to a multisensory system for an industrial robot. The received voice information isidentified, and executable instructions of the industrial robot are generated; the image information of a working environment of the industrial robot is acquired, and the movement behavior of the industrial robot is adjusted in real time according to the image information; a joint torque is obtained by acquiring a current in a servo motor, and closed-loop force-control dragging teaching is carried out; whether collision occurs or not is judged by calculating an external torque applied onto the industrial robot in real time; and data information is interacted in real time, and data processingand control are carried out, so that a traditional industrial robot has the teaching convenience and the running safety of a collaborative robot, and reserves the high speed and the high accuracy of multi-sensing for torque, sound and vision.

The invention discloses a method for describing movement behavior of a luggage image based on constant dimension and variable characteristics. The method comprises the steps of performing single frame image luggage detection algorithm; combining the luggage positions in the single frame into the whole luggage image movement behavior model based on constant dimension and variable characteristics. The method has the advantages that the influence of the change of the size, color and position of the luggage image caused by activating related image processing function on the luggage movement can be overcome, the any time of the process from the luggage image appearing to disappearing can be accurately extracted and displayed through a screen; an external luggage matrix is utilized to describe the luggage positions so as to describe the luggage image movement behavior model. With the adoption of the method, the common characteristics of the same luggage in two frame images can be found out under the influence of various image processing operations on the luggage image, and therefore, the luggage image movement description is achieved.

Method for estimating movement behaviour of a user of a mobile communication device by a neural network comprising one or more lower and one or more higher hidden layers. The method comprising a stepof obtaining (401) sensor data from sensors in the mobile device; a step of obtaining (402) measurements related to a movement of the user; a step of labelling (403) these measurements as weakly labelled data; pre-training (404) the lower hidden layers to estimate the measurements from the first set of sensor data; a step of obtaining (405) a second set of sensor data wherein movement behaviour ofthe user is labelled as labelled data; a step of training (406) the higher hidden layers with the labelled data to estimate the movement behaviour of the user as said output.

The invention provides a continuous dynamic movement and physiological data processing and analyzing system and method. The system comprises a movement big data preprocessing module, a continuous dynamic data feature acquisition module, a behavior recognition module, a behavior evaluation module, a scheme formulation module and an online and real-time intervention module, wherein the movement bigdata preprocessing module collects movement and physiological data and carries out preprocessing; the continuous dynamic data feature acquisition module extracts distribution features from the movement data; the behavior recognition module is used for building a movement model based on the distribution features and carrying out behavior recognition; the behavior evaluation module calculates the evaluation value of a movement behavior for the movement data; the scheme formulation module sets a movement data target scheme; a motion persistence monitoring module calculates the execution degree and the evaluation value of the execution degree based on the new data executed in the scheme; and the online and real-time intervention module resets the motion data target scheme based on the evaluation value of the execution degree. The technical scheme of the invention sets the new motion and physiological data processing method and a key quantity for measurement, is applicable to real-time processing and information feedback for massive motion data and improves the user experience.

The invention provides a symmetrical rimless wheel model motion behavior adjusting method based on a motion stopping attraction domain. The method comprises the steps: constructing and describing theinitial state of a symmetrical rimless wheel model based on the initial kinetic energy and slope inclination angle of the symmetrical rimless wheel model, creating a state coordinate system, and enabling an interval formed by the state coordinate system to be an attraction domain region; classifying the final stable state attraction domain according to the model parameters, and constructing and obtaining a corresponding attraction domain area in the state coordinate system; calculating boundaries among a plurality of different attraction domain areas; according to the initial state and the attraction domain area, jointly calculating a final stable state of the symmetrical sideless wheel model; and enabling the final stable state to meet the expectation by adjusting model parameters. According to the method, a complete adjustment method capable of realizing expected motion characteristics is constructed based on calculation and analysis of a stop motion attraction domain of the symmetrical rimless wheel model, and a practical scheme is provided for guiding biped walking simulation research of a real robot by using the rimless wheel model.

A system adapted to augment movement behavior of participants in an open motor task or activity includes one or more movement sensors configured to generate output characterizing movements of participants, including relevant interactions with elements and features of the environment and task or activity objects within the environment. A processor is configured to extract and segment a sequence of movement behavior elements from the output, register the movement elements with respect to operating environment, including the task or activity objects, recognize activity state, and determine cues to enhance performance and / or learning. Augmentations include verbal, visual, or haptic or audible signal-based cues that are designed to target critical aspects of movement skills in open motor tasks, including planning sequence of movements toward task goals; coordinating and executing movement elements in the sequence in relationship to relevant task activity events and elements.

The invention discloses a multi-view indoor pedestrian tracking method based on a movement behavior mode. The method comprises the steps of predicating the target movement state through a state space model and observing the predicated movement state through an observing model to obtain the tracking result, wherein the predicating includes that calculating the resultant force applied to a target in a testing video frame at the time t(i) ( / i), calculating the probable force of the target in the testing video frame at the time (i) ( / i), and predicating the target movement state at the next time according to the probable force of the target. With the adoption of the method, the validity of the pedestrian tracking information can be improved, and the robustness and accuracy of pedestrian tracking can be improved.

A mobile medical device drivable on rollers or wheels on the floor by way of a motor is provided, wherein the medical device is configured to take a planned route. The device has an optical display apparatus embodied to optically display at least one future movement feature of the medical device on the planned route. A visualization of the route planning helps the user to better estimate the future movement behavior of a medical device.

The invention discloses a human body identification method based on a sequence depth image, comprising: collecting the sequence depth image of a human body, and using a convolutional neural network and a recursive neural network to perform regression of human body joint points based on the sequence depth image to obtain the coordinate positions of the human body joint points ; Use the coordinates of the joint points of the human body to calculate the body posture feature vector, use the static feature classifier to identify, and obtain the human body identity recognition score of the static feature; extract the human body motion behavior feature from the sequence depth image, and use the dynamic feature classifier to perform Recognition to obtain the human body recognition score of dynamic features; the weighted calculation of the static and dynamic features of the human body recognition score to obtain the human body recognition score, and then obtain the human body recognition result. The present invention has strong robustness, and the accuracy rate is not affected by application scenarios.

The invention relates to the technical field of motion behavior recognition, in particular to a method and device for animal motion behavior discrimination based on temporal correlation analysis. Obtain the known continuous motion state of the animal and the corresponding three-axis acceleration information, as well as the continuous three-axis acceleration information to be discriminated; convert the three-axis acceleration information into three-dimensional data in the natural coordinate system, and the three-axis acceleration of the known motion state The information and the corresponding three-dimensional data constitute the training data, and the continuous three-axis acceleration information to be judged and the corresponding three-dimensional data constitute the test data; according to the time series correlation relationship between the motion behavior states and the iterative regression tree model obtained through the training data training, the test The data is used to discriminate the state of motion behavior, combined with the timing of the behavior state, it makes up for the shortcomings of the iterative decision tree's poor performance in processing highly relevant data, and further improves the accuracy of behavior discrimination.

The invention belongs to the field of mobile robot formation motion planning, and specifically relates to an improved adaptive zero-space behavior fusion method for a multi-robot formation, comprising the following steps: decomposing the formation motion process of robots into three types of motion behaviors: running towards a target point, avoiding Obstacles, formation; determine the execution order of 3 kinds of motion behaviors, and its priority order is to avoid obstacles, rush to the target point, formation; establish the motion model of each behavior; solve the behavior motion model according to the robot motion information; the method of the present invention Based on the traditional zero-space behavior fusion method, a logic-priority obstacle selection strategy between robots is proposed in the obstacle avoidance behavior, which effectively avoids the "overshooting" caused by double selection when two robots are obstacles to each other. The phenomenon of "avoiding the distance" and deviating from the mission route improves the efficiency of multi-robot formation missions, is conducive to the maintenance of formation formations, and has made significant progress in motion planning.

The invention provides a somatosensory motion simulation system. The simulation system comprises a user gravity center movement data acquisition unit, a standard movement model generation unit, a processing unit, a safety protection unit and a teaching unit. According to the simulation system provided by the invention, the gravity center movement data of a user can be collected in real time, whether the movement of the user is standard or not is analyzed according to the gravity center movement data of the user, when great deviation exists between the movement of the user and the standard movement, prompt is given out or the on-going movement is controlled, so that the movement behavior of the user is corrected and improved, further, the user masters the essential of the movement rapidly,therefore, the safety of the movement is guaranteed, in addition, the system has the functions of timely protection, movement body sensing and motion load generation and management, thus the user canbe helped to overcome the psychological disorder, the waist, back and leg strength is increased, and the training comprehensive effect is promoted.

The invention provides a movement behavior recognition method based on an axial acceleration sensor. According to the existing movement behavior recognition method based on multiple sensors or multiple axial sensors, data of multiple sensors or data of multiple axial sensors are required to combine, accordingly high computing cost is resulted, cost is increased, and meanwhile, real-time recognition is reduced. The method includes adopting axial data information of one acceleration sensor, extracting human body features, particularly three feature values of threshold in front of the peak of wave, threshold in front of the trough of wave, and time interval between the peak of wave and the trough of wave, in movement by waveforms formed through comparing the axial data information acquired by the sensor, and realizing accurate recognition of movement behaviors like squat, jump, run and the like. By the aid of the method, high recognition accuracy is achieved, computing cost is reduced, real-time recognition is improved, and cost is reduced.

The present invention discloses a method and system for recognizing the movement behavior of a legged robot based on multi-modal perception, wherein the method includes: based on various types of sensors, collecting information obtained by the legged robot repeatedly walking a preset distance on different types of ground The generated multi-modal sensing data information; the multi-modal sensing data information is divided into data sets according to different types of ground to obtain multi-modal sensing data sets of different ground types; multi-modal sensing data sets based on different ground types The sense data set trains the hidden Markov model of the viscous hierarchical Dirichlet process to obtain the hidden Markov model of the optimal viscous hierarchical Dirichlet process; input the information of the samples to be identified into the hidden Markov model of the optimal viscous hierarchical Dirichlet process The husband model is calculated, and the sum of the logarithmic likelihood function values ​​is calculated; based on the calculation results, the movement behavior recognition of the legged robot is carried out. In the implementation of the present invention, the reliability and accuracy of motion behavior recognition are improved.

The invention discloses a construction method of a video detection model of Parkinson's disease motion retardation based on a deep neural network, which combines computer vision and deep learning technology, evaluates the motion pattern of a test subject through short videos, and judges that there is motion Possibility of delayed symptoms. The present invention considers both the movement behavior and the movement process, proposes a detailed data collection method and a movement trajectory definition method, and designs three novel metrics and a periodic movement network model PMNet to deal with the problem of bradykinesia symptom judgment. Different from the traditional method, the present invention has the characteristics of scalability and portability. The key point extraction method based on the convolutional neural network can be replaced by other more accurate models, and more features can be added to describe the motion behavior. In addition, the present invention is capable of performing other similar periodic motion assessments, such as finger tapping actions of item 3.4 of the MDS-UPDRS scale.

The present application is applicable to the field of robotics technology, and provides a self-checking method. The self-checking method is applied to a robot. The self-checking method includes: if self-checking trigger information is detected, acquiring actual motion data of the robot; The actual motion data and the reference motion data are used to obtain a motion behavior self-check result, and the reference motion data is motion data collected when the robot is in a normal motion behavior state. Through the above method, the movement ability of the robot can be known in time.

This application provides a robot motion behavior control scheme, which sets a motion behavior task stack constructed according to the principle of first-in-last-out, which is used to store each motion behavior planning module that controls the robot's behavior, and iteratively traverses all processing tasks in an iterative manner. The motion behavior planning module in the motion behavior task stack, so that each motion behavior planning module in it is driven by input information to perform collaborative processing until the motion behavior task stack is empty, thereby realizing the motion behavior control of the robot. As a result, the control process of motion behavior can be reused and expanded in a modular and hierarchical manner, and the same motion behavior planning module can be called with different logic in different control tasks, which is conducive to improving the versatility of the scheme. Reduce development difficulty.

The invention relates to the field of multi-robot formation control, in particular to a multi-robot formation adaptive zero-space behavior fusion method. According to the robot formation movement intention, the robot movement process is decomposed, the behavior sequence steps are determined, and then the movement model steps are established, and the execution sequence of the three movement behaviors is solved to obtain three movement models. Motion information, solve to get the gain coefficient, and fuse the motion behavior steps to get the final speed and direction. Compared with the traditional zero-space behavior fusion method, the present invention makes the solved speed not changed by the working condition environment, has good adaptability, and takes into account both efficiency and performance, and can effectively control the speed. with significant progress.