18 results about "Motor execution" patented technology

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.

The invention discloses a motion execution mechanism error determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring instruction data corresponding to a motion control instruction of a motion execution mechanism; collecting displacement data of the motion execution mechanism in the process of executing the motion control instruction; and determining the motion error of the motion execution mechanism according to the instruction data and the displacement data. According to the scheme, an error analysis mechanism based on the instruction data and the actual displacement data is introduced, so that the motion performance of the motion execution mechanism is accurately evaluated and quantified. According to the scheme, the positioning precision and the overall operation stability of the motion executing mechanism can be remarkably improved, and the requirements of scenes such as high-precision industrial automation, robot operation and precision machining can be met.

The ability to effectively communicate information to people without causing them any discomfort or unease. [Solution] The robot operation method includes a detection step in which a detection device detects human movement, and an operation step in which a plurality of robots perform actions in accordance with the movement detected by the detection device, wherein in the operation step, at least one of the plurality of robots performs an action that is deviated from the actions of the other robots.

This invention relates to the field of correction control technology, specifically to an automatic correction control system for cotton web width on a drawing frame. The system includes a position detection module, a main control unit module, an auxiliary algorithm module, a drive control module, a motor execution module, a human-machine interaction module, and a process database module. The position detection module is used to collect the position and width information of the fed sliver in real time and convert it into a pulse equivalent signal. This invention uses a position detection module with dual sensors symmetrically arranged to collect sliver position and width information in real time and convert it into a digital pulse equivalent signal, improving the accuracy and reliability of cotton web status detection and providing high-quality data support for subsequent intelligent calculations. The auxiliary algorithm module integrates feature extraction, similar working condition matching, benchmark value calculation, dynamic correction, and constraint verification to achieve adaptive matching of correction parameters with multiple working conditions and continuously improve the accuracy of cotton web process distance calculation and the rationality of correction actions.

A control device is for controlling an industrial machine. The control device has a display control unit that divides a screen displayed on a display unit into a plurality of regions and displays representations, on the regions, concerning respective motion executions of the machine, a detection unit that, when a plurality of the representations are displayed in a combination, detects operation of a user on the machine or detects a time period in which a display pattern indicating the combination of representations has been displayed without change, and a saving unit that, when the detected operation is a predetermined operation having been set in advance or the display time period of the detected display pattern is equal to or longer than a predetermined time period having been set in advance, saves the display pattern when the detection has been performed as an intended display pattern.

This disclosure relates to a method, apparatus, electronic device, and storage medium for configuring virtual object actions, belonging to the technical field of virtual model processing. The method includes: acquiring action material of a target virtual object output by a user; invoking an intelligent agent to determine a group of motion execution units reflecting the motion relationship chain of the target virtual object in the action material; determining a first motion model matching the motion execution unit group and a mapping unit group mapped by the motion execution unit group on the first motion model in a preset motion model library; and modifying the motion execution unit group relative to the mapping unit group according to the configuration parameters of the action indication output by the user on the mapping unit group to obtain the modified motion execution unit group.

The invention discloses a desktop self-balancing education programming robot based on AI interaction. The system comprises the following modules: A, a visual perception module; b, a voice module; d, data transmission; e, an attitude sensing unit; f, a balance control unit; and G, a motion execution unit. The method has the advantages that natural language instructions, visual programming card recognition and automatic logic generation are fused, human-like communication type programming interaction is achieved, the learning threshold is remarkably lowered, and the method is suitable for being used by all-age users, especially low-age users; through real-time visual identification of a specific programming card and dynamic combination with a voice instruction / programming task, a physical-digital fusion teaching mode which can be flexibly configured and is highly situational is created, and immersion and interactivity of learning are greatly enhanced.

1. Name of the design product: anthropomorphic robot with two arms. 2. Use of the design product: to imitate human arm movements to perform various operations. 3. Design features of the design product: in shape. 4. Picture or photograph best indicating the design features: perspective view.

The application is a kind of self-organizing diffusion method and system for perception-limited cluster robots, belonging to the field of robot technology. The method steps are as follows: installing each single computer perception module; starting diffusion instruction, triggering the motion execution module according to the signal sent by the perception module on the control center of the single computer; the motion execution module performs motion according to the simplified self-organizing diffusion model P=(v o ,ω o ,v l ,L) until there is no perception target within the perception distance of all robots in the cluster, i.e. the diffusion is completed. The application solves the self-organizing diffusion problem of cluster robots in complex environments such as water, which cannot communicate, has no global positioning ability, has very low perception ability, and has very low robot computing power. Whether there is a perception target in the range is used as the diffusion execution condition, which simplifies communication and control equipment, shortens response time, and ensures uniform coverage in the target area and the timeliness of diffusion.

The application provides a method for solving the problem of live tracking gimbal target loss: S1 acquires video image data; S2, tracking condition a: if the determination result is TRUE, directly enter the KCF tracking module, otherwise enter S3 judgment; S3 motor stop state: if TRUE, enter S4, otherwise continue to execute S1; S4 detection link; S5 tracking condition b: if the tracking condition b is TRUE, enter S6, otherwise continue S1; S6 tracking link: including KCF tracking module, tracking verification module, verification frequency setting module and updating tracking condition a; wherein the tracking verification mechanism composed of the tracking verification module and the verification frequency setting module adopts a local CNN human body detection algorithm, verifies the actual size of the human body frame and synchronously updates the KCF parameters, and only performs human body detection in the region of interest; S7, gimbal control link: including calculating gimbal control signal module, motor execution module, acquiring motor state module; and updating motor stop state. Solve the problem of tracking target loss.

The invention relates to the technical field of elderly mobility scooters, in particular to an intelligent elderly mobility scooter system integrating electroencephalogram and electromyographic signal monitoring and multi-mode control, comprising a signal acquisition module used for acquiring an electroencephalogram signal and an electromyographic signal of a user; the signal processing module is used for analyzing and processing the signal and generating control information representing the motion intention and the motion state of the user; the control decision module is used for generating a motion control instruction according to the control information, and limiting, adjusting or suppressing the instruction when it is detected that the electroencephalogram signal and the electromyographic signal do not meet a preset coordination condition; and the motion execution module is used for driving the elderly scooter to execute corresponding advancing, steering or braking operation according to the motion control instruction. Through collaborative acquisition and consistency judgment of the electroencephalogram signals and the electromyographic signals, direct and reliable capture and cross validation of the control intention of the user are achieved, an active safety barrier is constructed fundamentally, and the misoperation risk is remarkably reduced.

The application discloses a motor driving sudden change friction characteristic modeling and feedforward compensation method, which realizes decoupling and independent description of non-sudden change characteristics and sudden change characteristics of friction, and constitutes a dynamic neural network friction model with a width neural network structure which integrates the width neural network structure characteristics and the dynamic recurrent neural network structure characteristics; and the motor control system feedforward control compensation based on the friction model: the motor rotation angle is used as the input signal of the friction model, the friction model obtains corresponding friction torque prediction and estimation, the output of the motor controller is corrected through the feedforward control compensation, and the influence of internal friction on the motor execution precision is offset.

The invention relates to the technical field of artificial intelligence, and discloses an interaction control method based on a spiking neural network, which comprises the following steps: acquiring a multi-modal signal, and preprocessing the multi-modal signal to generate a multi-modal data set; inputting the multi-modal data set into a pulse neural network, and outputting a motion execution instruction and a user intention feature; inputting the multi-modal data set into a pulse convolution capsule network, and outputting an emotion classification result; generating a multi-modal feedback strategy according to an emotion classification result and the user intention features through a pulse reinforcement learning engine; and controlling a robot and / or a mechanical arm to execute actions based on the motion execution instruction, and driving a virtual avatar module and a multi-sensory feedback module to execute an emotional operation in combination with a multi-modal feedback strategy. According to the method, EEG signals and robot sensor data are fused through the pulse neural network, emotion recognition, driving virtual avatar and multi-mode interaction are achieved, and high-emotional and low-burden accompanying experience is provided for the disabled and the like.

The invention relates to the field of gesture recognition interaction, in particular to a gesture interaction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current virtual interaction object; if the left hand and the right hand are both in the preset gesture triggering state, acquiring first motion information of the left hand and the right hand, and determining a current motion execution state according to the first motion information; and based on the motion execution state, obtaining second motion information of the left hand and the right hand, and controlling the current virtual interaction object to move according to the second motion information. Compared with the prior art, judgment of the motion execution state and specific execution are separated, so that execution of specific motion can be carried out based on the currently determined motion execution state every time, and the situation that when the motion execution state and execution are carried out at the same time, the execution time is shortened is avoided. The situation that the motion execution state is misjudged due to shaking of the hands of the user is avoided, so that the interaction experience of the user is effectively improved.

The invention provides an Alzheimer's disease rehabilitation training system based on a sense-motion-cognition coupling mechanism, which comprises a sense guide module, a motion participation module, a cognition intervention module and a coupling regulation and control module, and is characterized in that the sense guide module is used for providing sense input related to a training task; the exercise participation module is used for guiding the subject to complete an exercise task matched with the sensory stimulation; the cognitive intervention module is used for embedding cognitive processing and task decision links in the sensory-motion interaction process; and the coupling regulation and control module is used for carrying out coordinated regulation on the action sequence, duration and task difficulty of each module according to the reaction state, participation degree or completion characteristics of the subject in the training process, so that a stable, continuous and adaptive coupling relationship is formed in the same training process by sensory input, motion execution and cognitive processing.

The invention discloses a spasm detection system and method based on a rehabilitation robot. The system comprises a motion execution module; the information acquisition module is used for acquiring the actual position and the actual output force of the preset part of the motion execution module in the preset time; the motion planning module is used for sending a control signal to the motion execution module or the spasm detection module; the spasm detection module is used for calculating the comprehensive spasm degree of the patient according to the offset between the actual position and the preset position and the difference value between the actual output force and the preset maximum output force, and when the comprehensive spasm degree is larger than the preset comprehensive spasm degree, the motion execution module is controlled to take corresponding spasm treatment measures. The spasm detection module can judge whether the patient has spasm or not based on the offset between the actual position of the preset part and the preset position, so that the rehabilitation robot can be controlled to take corresponding measures, and secondary injury to the patient is avoided.

The invention belongs to the technical field of interactive rehabilitation robots, and discloses a three-channel electroencephalogram signal driving-based hand rehabilitation system, which comprises an upper computer, a lower computer, an execution mechanism and an electroencephalogram signal sensor, wiFi communication is adopted between the lower computer and the upper computer; the invention further discloses a hand rehabilitation method based on three-channel electroencephalogram signal driving, and the method comprises the following steps: S1, a subject wears the device according to requirements, sets the upper computer as a training mode, opens normal form presentation, and collects 50 groups of resting state training data and 50 groups of task state training data; s2, training a classification model by using the collected training data, and deploying the model on an upper computer; s3, the upper computer is set to be in an online mode, electroencephalogram signals are collected and recognized according to a time window, and a classification result is output to serve as a lower computer control instruction; s4, the upper computer is set to be in a training mode, and 50 groups of training data of gripping actions and passive exercises of the opposite side hands of the subject are collected respectively; s5, training a classification model by using the collected opposite side hand grasping action motion execution and passive motion training data, and deploying the model on an upper computer; s6, the distribution condition of passive movement and movement execution classification results in the movement period is checked, and rehabilitation effect evaluation indexes are output; according to the interactive brain-computer interface rehabilitation robot system based on portability, easy deployment, recognition effectiveness and interactivity, the practical application significance of a hand rehabilitation system driven by electroencephalogram signals is improved.

The present application relates to the technical field of screw installation, and discloses a screw machine positioning mechanism, which comprises a rack, a mounting plate two, a motion execution system, a vision system and a moving speed regulation system. The motion execution system drives the mounting plate two to translate along the XYZ axis; the vision system collects positioning images; the moving speed regulation system comprises a mechanical dynamic performance evaluation module, a motion path evaluation module, a vision system confidence evaluation module, a negative pressure state evaluation module and a moving speed regulation module. Through the collaborative work of multiple modules, the mechanical performance coefficient, the motion path coefficient, the vision confidence and the negative pressure matching coefficient are output based on the parameters of vibration acceleration, tracking error, load, path curvature, moving distance, image definition, illumination intensity, real-time negative pressure value and vacuum establishment time, and finally the moving speed is dynamically adjusted in real time through the speed regulation model. The present application realizes the intelligent balance of positioning accuracy and efficiency under high-speed operation.