18 results about "Gait phase" patented technology

The application discloses a portrait inspection and identification method based on gait cycle decomposition and multi-phase force analysis, and comprises the following steps: extracting the skeleton key points of a target object in a target object image set in a comparison video and the confidence score of each skeleton key point; extracting the features of the target object in the target object image set according to the skeleton key points of the target object, and obtaining the target object features, wherein the target object features comprise basic features, gait cycle features, gait stage features and gait parameter features; integrating the basic features, the gait cycle features and the gait stage features to obtain comprehensive features; respectively calculating the similarity of the target object features and the comprehensive features in the comparison video to obtain similarity calculation results and overall similarity calculation results; and obtaining the identity identification result of the target object according to the similarity calculation results and the overall similarity calculation results.

This invention relates to the field of quadruped robot navigation technology and discloses an intelligent navigation system for quadruped robots that combines behavior tree and visual recognition. The system includes modules for path processing, turn prediction, behavior tree decision-making, obstruction judgment, visual recognition and language evaluation, and navigation decision-making. The path processing module integrates multi-sensor data to acquire pose and gait state; the turn prediction module calculates safe turning speed and trigger distance based on a gait phase sliding window; the behavior tree decision-making module dynamically adjusts node priorities according to trigger intensity, and the switching timing is controlled by a gait phase arbiter; the obstruction judgment module generates path obstruction flags; the visual recognition module adopts a two-level architecture, calling a visual language model to evaluate the landing area for static traversable obstacles; and the navigation decision-making module integrates landing semantic confidence and outputs commands to cross, detour, wait, or return. This invention enables quadruped robots to achieve proactive predictive navigation and intelligent obstacle avoidance decision-making in complex industrial environments.

This application discloses a method and system for controlling a lower limb exoskeleton using a brain-computer interface based on intent confidence, relating to the field of medical rehabilitation technology. First, the system identifies the original electroencephalogram (EEG) signal to obtain intent confidence, and maps an impedance parameter set including joint stiffness and damping coefficients by combining gait phase information. Second, it extracts motion position and interaction force errors, compares the intent confidence with the actual execution state using a sliding time window to obtain neural matching errors, and merges these three into a total composite error. Simultaneously, it assesses human-machine coupling compliance based on changes in human joint angles and exoskeleton torque, and generates a safety gain coefficient. Finally, it performs gain calculations on the total composite error based on the impedance parameter set, and corrects it with the safety gain coefficient to obtain the target control torque for output to the actuator. This constructs a neural-force-motor three-loop architecture, achieving compliant and adaptive on-demand assistance and highly safe human-machine collaborative rehabilitation with defensive protection.

This invention discloses a gait prediction method and system based on a support-aware multi-scale temporal convolutional network. The method includes the following steps: S1, collecting plantar pressure data through pressure insoles and ground reaction force data through a force plate to construct a gait dataset; S2, performing data preprocessing and feature enhancement on the dataset from step S1; S3, establishing a multi-scale support-aware temporal convolutional network (MSA-TCN) model; S4, training the network model from step S3 using supervised learning, inputting the dataset from step S2 into the trained network model for prediction, and outputting the predicted ground reaction force. This invention overcomes the shortcomings of existing technologies in multi-scale temporal feature extraction, gait phase-aware modeling, and synchronous high-precision prediction of multi-dimensional mechanical parameters.

The application relates to the technical field of behavior feature recognition, and discloses an anti-crosstalk biological rehabilitation electrical signal safety control method and system in a high-density scene. The method comprises the following steps: calculating a bilateral average distance according to an environmental distance; calculating a surface electromyogram signal root mean square value according to an electromyogram original signal; analyzing a pelvis tilt angle standard deviation according to the pelvis tilt angle; calculating a hip joint adduction auxiliary torque according to the surface electromyogram signal root mean square value and a gait phase; calculating a hip joint coronal plane impedance value according to the gait phase and the pelvis tilt angle; calculating an intention state according to the surface electromyogram signal root mean square value, the pelvis tilt angle and a channel state; calculating a hip joint driving torque according to the hip joint adduction auxiliary torque and the hip joint coronal plane impedance value; and calculating a system mode according to the bilateral average distance, the surface electromyogram signal root mean square value and the pelvis tilt angle standard deviation. The application improves the lower limb rehabilitation training efficiency by adaptively adjusting the gait.

This invention provides a motion environment perception method based on biomechanical features and gait adaptation, belonging to the field of human-computer interaction perception. The method includes: acquiring the subject's original motion feature sequence and biomechanical feature vector; scaling and aligning the physical dimensions using a biomechanical scaling matrix to obtain a motion feature tensor; inputting the motion feature tensor and biomechanical feature vector into a multi-task temporal convolutional network for feature-level linear modulation to obtain modulated deep features; processing these features through a backbone network, with the classification branch outputting terrain categories and the regression branch outputting initial values ​​of environmental geometric parameters; freezing the backbone network parameters; fine-tuning the regression branch based on an adaptive loss function driven by gait phase and foot arch features to obtain a motion environment perception model; inputting real-time data from the subject; and outputting terrain categories and environmental geometric parameters. This invention solves the technical problem of low accuracy in environmental geometric parameter estimation caused by biomechanical differences between individual subjects in existing technologies.

The application discloses a gait evaluation method and device, electronic equipment and storage medium, and relates to the technical field of gait analysis. In the application, gait data of a person with motor dysfunction in adjacent multiple walking cycles is obtained; the gait data comprises a gait phase parameter set and a posture angle parameter set of the person with motor dysfunction; from the gait phase parameter set and the posture angle parameter set included in the gait data, a gait phase parameter subset and a posture angle parameter subset corresponding to each of multiple gait evaluation dimensions are determined; based on the gait phase parameter subset and the posture angle parameter subset corresponding to each of the multiple gait evaluation dimensions, gait evaluation of the person with motor dysfunction is performed in the multiple gait evaluation dimensions to obtain multiple gait evaluation values; and based on the multiple gait evaluation values and gait evaluation weight values corresponding to the multiple gait evaluation values respectively, a comprehensive gait evaluation value of the person with motor dysfunction is determined. In this way, the gait data of the person with motor dysfunction is comprehensively quantified, and the accuracy of gait evaluation is improved.

This invention relates to the field of assistive medical technology, specifically to an assistive walking device and its monitoring system for postoperative hip replacement patients. The device includes a data acquisition module, a gait segmentation module, a posture assessment module, a behavior recognition module, and a monitoring instruction module. The data acquisition module collects a set of lower limb kinematic data from the patient. The gait segmentation module performs gait phase segmentation on the data, generating gait cycle division results. The posture assessment module uses a multilayer perceptron to construct a walking posture assessment model, analyzes joint stress on the gait cycle division results, and generates peak hip joint stress and compensatory knee flexion angles. The behavior recognition module identifies prohibited behaviors based on the above indicators, generating violation action markers and spatial coordinates. The monitoring instruction module generates a set of monitoring instructions, including voice prompts and vibration alert intensity parameters. This system enables precise monitoring of postoperative walking for hip replacement patients, assisting in standardized patient rehabilitation.

This invention discloses a terrain-adaptive control method for quadruped robots based on local virtual plane estimation, belonging to the field of quadruped robot motion control technology. The method acquires the body state, gait phase, and foot contact information to construct a set of support contact points. It weights the contact points based on contact confidence, normal contact force, and contact point timeliness to estimate a local virtual plane. It extracts slope angle, height difference, and height dispersion to construct terrain geometry indices. Based on these indices, it adaptively adjusts the body's center of mass height, body posture, and swing leg landing point reference, and dynamically updates the priority or weight of whole-body control tasks, achieving stable motion control in complex terrain. This invention improves the robustness of local terrain estimation and the terrain adaptability of quadruped robots.

The present application relates to the technical field of exoskeleton control, and discloses an exoskeleton gait adaptive adjustment method based on terrain pre-perception, comprising: acquiring real-time data of an inertia measurement unit and a distance sensor at a foot and a lower leg, including acceleration, angular velocity and foot-ground distance; fusing to generate a foot posture angle change sequence, a foot-ground distance change rate, a hip joint angle and a knee joint angle; calculating a tilt angle and a change trend of a front ground according to the posture angle change sequence and the distance change rate, and predicting a slope change in a future gait cycle; determining a current gait phase according to the hip and knee joint angles; calculating an adjustment amount of joint torque required to maintain the stability of the center of gravity based on the tilt angle, the slope change and the gait phase; generating and executing control instructions at different times in the next gait cycle to drive the joint motor to output auxiliary force. The present application can predict the front slope before the foot touches the ground and adjust the assistance strategy in advance, maintain the stability of the center of gravity, and reduce the risk of falling.

This invention discloses a motion control method for humanoid robots based on ground contact detection, applied to bipedal humanoid robots with a floating base structure. The method includes: constructing a model predictive controller based on a center-of-mass dynamics model; constructing a ground contact probability model; adaptively adjusting the current gait phase based on ground contact detection results; and constructing an inverse dynamics quadratic programming problem including generalized acceleration, foot contact force, and joint driving torque, solving for the joint torque command under conditions satisfying dynamic constraints, foot non-slip constraints, and friction cone constraints. By introducing ground contact detection into a nonlinear model predictive controller, designing a reasonable center-of-mass dynamics model, and adding necessary equality and inequality constraints, this invention enables adaptive adjustment of the humanoid robot in scenarios with unknown foot contact states, improving the stability and robustness of the humanoid robot's walking in complex terrain conditions.

The application relates to a millimeter wave radar non-contact gait and body analysis method and device for spinal degeneration and instability, wherein the method comprises the following steps: millimeter wave radars are arranged on the front side and the rear side of the walking path of a subject, echo signals are collected when the subject performs straight walking, axial rotation and forward and backward bending actions, and a sub-millimeter precision point cloud atlas is generated. Based on spinal anatomical landmark points and body surface contour curvature extrema, the point cloud atlas is divided into rigid motion units such as cervical vertebrae, thoracic vertebrae, lumbar vertebrae and sacral and pelvic units, and the spatial position sequence and the Euler angle sequence of six degrees of freedom of each unit are extracted. By calculating the relative displacement vector and the rotation angle between adjacent segments, three-dimensional intervertebral motion time sequence signals are generated, a thoracolumbar motion trajectory spherical projection map is constructed, and the segment motion characteristics in the gait phase are analyzed. The application dynamically captures sub-millimeter three-dimensional intervertebral data non-contact, accurately quantifies hidden instability, compensation and locates responsible segments.

This invention discloses a method and system for gait phase prediction and assist distribution for lower limb exoskeletons, belonging to the field of exoskeleton robot control technology. It extracts spatiotemporal features and predicts gait phases from human gait temporal motion data using a spatial graph convolutional network and a directed acyclic graph model of the lower limb skeleton. The lower limb skeleton's directed acyclic graph model captures spatial features of the human gait phase, achieving spatiotemporal fusion prediction of the gait phase. Furthermore, a gait pattern mapping relation library is used to perform continuous matching of gait patterns, thereby analyzing accurate and reasonable predicted gait patterns. In addition, the predicted gait patterns are used to target the predicted assist torque sequence of the lower limb exoskeleton, achieving a precise and low-energy-consumption target active assist torque sequence. This allows for precise output distribution of assist through the exoskeleton motor driver, expanding the applicability of lower limb exoskeletons in complex walking scenarios and improving walking efficiency.

This application provides a method, device, equipment, and medium for active image stabilization and cooperative control of a quadruped robot, including: determining gait phase variables based on the quadruped robot's gait parameters and foot force data; predicting body posture vibration data within future gait cycles using a constructed body posture vibration prediction model with the gait phase variables as independent variables; generating a feedforward compensation signal based on the body posture vibration data; performing image stabilization control on the gait prediction feedforward outer loop and the gimbal native feedback inner loop based on the feedforward compensation signal and real-time posture data; determining a stable shooting range, and automatically triggering the gimbal to perform image acquisition when the gait phase enters the stable shooting range. This achieves a paradigm breakthrough from passive hysteresis stabilization to active feedforward image stabilization, significantly improving image quality and upgrading the inspection mode from the inefficient "walk-stop-shoot" to intelligent shooting during continuous walking through deep coupling of gait, gimbal, and shooting.

The application provides a hip exoskeleton control method and system for reaction control and complementary constraint, and the method comprises the following steps: S1, constructing a hip exoskeleton man-machine system dynamics model, and establishing a coupled dynamics equation; S2, establishing a foot-ground contact nonlinear complementary constraint model, and introducing a relaxation parameter for further processing; S3, establishing a multi-objective reaction control cost function, comprehensively considering an exoskeleton space, a human space, an environment space and a complementary constraint violation penalty term, and constructing a quadratic cost function; and S4, solving a quadratic programming problem with a complementary constraint in real time, dynamically adjusting the relaxation parameter according to the real-time estimated stiffness of the ground and the gait phase, and outputting a control instruction. The application is based on the online estimation of the stiffness of the ground and the adaptive parameter adjustment mechanism of the gait phase, can adapt to different ground characteristics and gait stage changes in real time, dynamically adjusts the complementary relaxation parameter, and makes the system show stronger adaptability and robustness in a complex walking environment.

This invention discloses a gait recognition system and method based on spatiotemporal block convolution and multidimensional feature fusion, belonging to the field of biometric recognition technology. The method includes: acquiring a gait contour sequence and preprocessing it to obtain a three-dimensional feature map; constructing a dual-path parallel branch, where the local branch performs multidimensional physical segmentation of the feature map in terms of time, height, and width, and independently convolves each spatiotemporal sub-block before in-situ splicing to restore it; and the global branch performs overall convolution on the feature map; after fusing the two features, the system is mapped using a spatial horizontal pyramid, dividing the feature map along the height direction into multiple horizontal strips with the same number of height segments as the local branch segmentation, and pooling to obtain part feature vectors; these are then compressed into fixed-length part features using temporal max pooling; multiple independent recognition sub-units are constructed with the same number of strips as the number of strips, each sub-unit receiving only the corresponding strip features for part-level identity discrimination, and the final recognition result is obtained through fusion. This method solves the problems of lost local details, gait phase interference, and weak spatial perception, improving recognition accuracy.

The application discloses a kind of four-wheel foot type robot adaptive control method based on reinforcement learning and NMPC-WBC, through gradient descent method and joint soft constraint combination, realize the autonomous start under the initial posture of robot and leg posture alignment, without foot end force sensor and external perception equipment, only through ontology multi-source state data, combined with reinforcement learning network realizes implicit contact sensing and terrain parameter estimation, reduce hardware cost, NMPC optimization module with dynamic soft constraint and time-event hybrid triggering mechanism cooperate, real-time adjustment gait phase and foot end trajectory, adapt to complex terrain change, improve motion stability, wheel-step switching WBC control model realizes the adaptive switching of pure wheel mode and leg-lifting wheel-step mode, WBC hierarchical force distribution strategy based on task priority, priority guarantee body posture and center of mass stability, improve control precision and system robustness, can be applied to various four-wheel foot type robot inspection, detection scene.

The application provides a stair climbing and descending assistance phase recognition method, system and computer readable medium based on a random forest, which comprises the following steps: acquiring wearing sensor data of different human bodies in the process of performing stair climbing and descending actions and non-stair climbing and descending actions; determining the foot movement state according to the plantar IMU sensor data, determining the movement gait phase and labeling; constructing a training set by using the IMU sensor data of the thigh and calf positions in the wearing sensor data and the labeled movement state; training a stair climbing and descending assistance phase recognition model based on a random forest classifier; and finally, determining the IMU sensor data of the thigh and calf positions in the actual acquired human body action by using the assistance phase recognition model, and outputting the corresponding movement assistance state. The method of the application can be used for recognizing the assistance phase in the process of stair climbing and descending, has a simple sensor layout, saves the time consumed in the data labeling process, and can improve the recognition accuracy.