947 results about "Walking gait" patented technology

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention discloses a quadruped robot inspection method based on multi-modal sensing fusion. The method comprises the following steps of: 1, initializing a global task, loading a basic navigation map, and generating the basic navigation map comprising topographic features, forbidden areas and parking positions; 2, autonomous navigation and dynamic correction of positioning deviation are realized according to laser radar SLAM data, and the positioning deviation is dynamically corrected according to real-time observation data; 3, dynamically switching or adjusting the gait strategy according to the terrain category, training the gait strategy of the quadruped robot to be matched with the terrain category, and generating a self-adaptive motion control instruction to adapt to the terrain in real time; and step 4, synchronously realizing parking area structured data acquisition and dynamic abnormal information perception through multi-sensor fusion, and realizing target state monitoring and abnormal event response in the inspection task. According to the invention, through collaborative innovation of the bionic motion platform and multi-mode intelligent detection, all-terrain coverage, total-factor perception and full-process autonomous intelligent inspection in a complex parking lot environment is realized.

The invention belongs to the field of robot motion control, and particularly relates to a humanoid robot multi-terrain gait control method and system fused with visual perception. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot, and preprocessing the multi-modal sensing data; the preprocessed multi-modal sensing data are input into a pre-trained world model, and the world model updates and outputs a potential state at the current moment based on a historical recursive state, a historical random posteriori state and an action sequence in a historical updating interval; inputting the potential state at the current moment into a pre-trained strategy network, and outputting an action at the current moment; and based on the action at the current moment, the driving torque of each joint is calculated through a PD controller, and the humanoid robot is driven to move. According to the method, a world model structure is introduced, so that the robot can realize more stable and more efficient gait control and terrain adaptation of the humanoid robot under the condition that the robot only depends on perception information which can be acquired by the robot.

The invention discloses a wearable plantar pressure and three-dimensional gait analysis system, and relates to the technical field of gait analysis, the system comprises a wearable in-shoe plantar pressure analysis subsystem and a three-dimensional gait analysis subsystem; the wearable in-shoe plantar pressure analysis subsystem comprises a pressure sensor module, a pressure integration module, an intelligent transmission module, a detection software module, a power supply module, an instantaneous pressure and balance detection module, a standing balance detection module, a balance and gait detection module and a cloud server; the three-dimensional gait analysis subsystem comprises a hardware unit and a software unit; according to the invention, a wearable plantar pressure and three-dimensional gait analysis collaborative system is constructed, bidirectional verification compensation is realized by means of data fusion of two subsystems, and traditional single-dimensional limitation is broken; pressure sensing and a double-view reconstruction technology are fused, data are dynamically captured, and bidirectional error correction is carried out; the limitation of traditional equipment is solved, the evaluation accuracy and robustness are improved, and a quantitative basis is provided for clinical diagnosis, rehabilitation and the like.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

The invention provides a gait monitoring system based on multi-modal sensing fusion, which comprises the following steps of: establishing an initial base line to form a personalized gait base line model; baseline updating is adopted, and after a system obtains new gait data, a weighted moving average algorithm is adopted for self-learning updating, so that the historical data weight is enhanced, and the baseline stability is improved. The technical problems of insufficient individual difference adaptation, lack of dynamic threshold adjustment, poor real-time performance and the like in the prior art are solved.

The invention belongs to the technical field of mobile robots, and particularly relates to an amphibious hexapod robot which comprises a robot body, six walking feet, a protection box, propellers, a multi-sensor system and a central control unit. The six walking feet are arranged on the two sides of the robot body, and a waterproof driving joint is arranged at the tail end of each foot. The protection box is arranged at the root of the walking foot and internally integrated with propellers; the sensor system comprises a water depth sensor, an attitude sensor and a grounding detection module; the central control unit is used for collecting sensor data in real time, judging whether the current environment is a land or a water area, automatically switching a walking mode or a water propulsion mode, and meanwhile adaptively adjusting gaits and the rotating speed of propellers according to the water depth and the terrain. The robot has environment perception, mode decision, gait generation, grounding prevention protection and energy optimization strategies, full-autonomous and high-adaptability movement in the land and water environment is achieved, and the working capacity and safety of the robot in the complex environment are improved.

The invention relates to a personalized exercise rehabilitation risk early warning method and device based on IMU dynamic instability assessment, and the method comprises the steps: collecting multi-dimensional exercise data of a subject, carrying out the posture fusion, and obtaining the limb posture, joint angle change rate and angular momentum of the subject; performing time sequence mode decomposition to extract a single gait cycle and a specific jump landing attitude; calculating a biomechanical feature vector of each motion primitive; inputting the single gait cycle, the specific jump landing attitude and the biomechanical feature vector into a hybrid injury induced motion model, and outputting a multi-dimensional injury risk index vector; and comparing the multi-dimensional injury risk index vector with historical health data and a moving target dynamic adjustment threshold value of the subject, and if the threshold value is exceeded, providing real-time auditory, tactile or visual feedback to the subject. According to the method, the influence of movement speed and rhythm change is overcome, dynamic instability in movement can be more sensitively reflected, and personalized risk assessment in a real sense is realized.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention discloses a dual-drive self-switching strong-adaptability quadruped robot motion control method, which comprises the following steps of: estimating current topographic features by using sensing signals such as body state feedback, IMU (Inertial Measurement Unit) and sole force, and predicting a falling probability according to the topographic features, so that a quadruped robot is intelligently switched between two driving modes of MPC (Moving Picture Control) independent control and MPC-DRL (Moving Picture Control-Digital Regulation Language) cooperative control; dynamic behavior parameters are introduced to influence the gait and step height of robot movement, the optimization efficiency and smoothness of MPC and the disturbance adaptive capacity of DRL are efficiently fused, and the high-robustness, high-efficiency and self-adaptive movement capacity of the quadruped robot in various terrains under the condition that external sensing information is not relied on is enhanced.

The invention discloses a high-precision indoor positioning system based on a smart bracelet, and the system comprises a data processing module which is used for obtaining and preprocessing multi-source sensing data collected by the smart bracelet; the gait reckoning module is used for executing gait event detection and pedestrian dead reckoning processing; the wireless observation module is used for carrying out preprocessing and credibility evaluation on indoor wireless positioning observation data; the fusion positioning module is used for carrying out credibility weighted fusion; the trajectory constraint module is used for applying trajectory rationality constraint and correcting abnormal position points; the error correction module is used for constructing a positioning error dynamic correction model and executing online correction processing; and the track output module is used for carrying out smoothing and time resampling processing and extracting a human body position determination result set. The method is based on the multi-source perception fusion and federated Kalman dynamic correction method, achieves the indoor high-precision positioning of the smart bracelet, and has the advantages of continuous track, controllable error and high environmental adaptability.

The invention discloses a composite type four-foot wall climbing robot integrating a fish suction cup adsorption mechanism and lizard crawling characteristics. The problems that a traditional wall climbing robot is poor in adsorption stability and insufficient in moving flexibility on uneven and wet wall surfaces are solved. The core structure of the robot comprises an adsorption sole assembly, a multi-degree-of-freedom leg joint, a bionic spine, a micro air pump and a sole pressure sensor, and the adsorption sole assembly controls an air bag through the air pump to generate negative pressure to adapt to various wall surfaces; multiple joints of the legs are matched with the spherical joints to achieve multi-angle attachment of the foot sole, the bionic spine can be bent and buffered, lizard diagonal gaits are adopted, the rotation angle of the joints is adjusted in combination with a sensor, self-adaptive movement on the wall with the inclination angle ranging from 0 degree to 90 degrees is achieved, and the efficiency is improved by 30% or above compared with a traditional robot. The robot breaks through the limitation of a single bionic mechanism, can be applied to complex high-altitude scenes such as building outer wall detection, and reduces the manual operation risk.

The invention relates to the technical field of biped robot reinforcement, and discloses a historical action sequence-based biped robot reinforcement learning control method and system. The biped robot reinforcement learning control method is applied to reinforcement learning control equipment and specifically comprises the following steps that S101, a floating base frame is adopted to load a biped robot URDF model in an Isaac Gym simulation platform, ten active joint freedom degrees are configured, and a control interface is constructed to output a 10-dimensional motor position command vector; the motion robustness and adaptability of the biped robot in a dynamic environment are remarkably improved by constructing a Transform time sequence modeling architecture fusing a historical state-action sequence, and the historical observation and action sequence is combined with sine-cosine position coding to be input into a multi-layer Transform encoder, so that the motion robustness and adaptability of the biped robot in the dynamic environment are improved. The self-attention mechanism is used for capturing the long time sequence dependence relation of foot-ground contact force change, topographic relief and the like, and the problem of gait instability caused by limited local observation in a traditional method is solved.

The invention relates to the technical field of robot control. A humanoid robot gait generation system based on hierarchical control is characterized by comprising a multi-layer control system operating at different refresh frequencies, and the multi-layer control system comprises a high-layer gait and trajectory planning layer operating at a low frequency and used for generating a macroscopic motion instruction; the middle-layer whole-body motion optimization layer runs at medium frequency, serves as a bridge of high-layer planning and bottom-layer control and is used for converting expected plantar and trunk tracks into reference motion of all joints of the whole body; the bottom joint servo control layer runs at the highest frequency and is responsible for accurately tracking a joint-level instruction output by the whole-body motion optimization layer; the state sensing and feedback closed-loop layer is used for feeding back state information, obtained in real time, of the body state of the robot to all the layers, providing a basis for track re-planning of a high layer and providing a real-time state initial value for optimization of a middle layer; and a feedback signal is provided for servo control of a bottom layer.

The invention relates to the technical field of medical health data analysis, in particular to a multi-modal data fusion abnormal behavior early warning system before falling of a high-risk patient. According to the method, for any patient, a plurality of gait cycles formed by time frames are obtained according to ankle coordinate distribution of different time frames; according to the ankle coordinate distribution of different time frames in each gait cycle, gait high risk of each gait cycle is obtained; according to the hip coordinate distribution of different time frames in each gait cycle, obtaining the center-of-gravity shift degree of each gait cycle, and obtaining the high-risk center-of-gravity shift reference degree of all patients in each gait cycle; for any patient, the falling probability in each sliding window is obtained according to the gravity center shift degrees of different gait cycles, the high-risk gravity center shift reference degree and various physiological parameter data at different moments; and early warning is carried out on abnormal falling behaviors. According to the invention, the accuracy of early warning of abnormal falling behaviors is improved by accurately analyzing the falling probability of the high-risk patient.

The invention relates to the technical field of hybrid intelligence, in particular to a quadruped robot motion control and operation system for an industrial environment, which realizes full-link intelligent management from environment perception to motion control, and remarkably improves the operation precision of a robot and the system efficiency. A three-dimensional map is constructed through a multi-sensor fused environment sensing module, and precise positioning is realized by combining state estimation based on deep learning; a motion control module with a hierarchical control architecture is adopted, and stable motion under a complex terrain is ensured through model prediction and gait optimization; the digital twinning and cloud side end collaboratively constructs a virtual-real mapping system to realize predictive maintenance and remote monitoring; and the multi-agent coordination module realizes task coordination and resource optimization of the multi-robot system through a distributed scheduling algorithm.

The invention provides an old people falling risk monitoring analysis method and system, and the method comprises the steps: collecting multi-source heterogeneous signals, such as plantar pressure distribution, attitude trajectory output by an inertial measurement unit, electroencephalogram attention concentration ratio, galvanic skin response and the like, carrying out data synchronization and spatial normalization processing through a unified timestamp, and carrying out data synchronization and spatial normalization processing; extracting gait gravity center tracks and physiological state features; the gait deviation degree is matched by constructing an individual behavior baseline library and a dynamic time warping algorithm, the cognitive load state is evaluated in combination with a lightweight LSTM network, physical instability and cognitive risks are subjected to multi-dimensional fusion, a falling risk score is output through a fuzzy logic engine, and the system can achieve graded early warning response and emergency positioning. According to the invention, the accuracy, adaptability and early warning efficiency of fall risk assessment are significantly improved.

The invention discloses a weight loss gait test evaluation and training device and method, and the device comprises a weight loss control module which is used for weighing the weight of a user in real time and automatically adjusting the air pressure of an air bag according to a set weight loss proportion; the gait information acquisition module is used for synchronously acquiring plantar pressure distribution, lower limb joint kinematics parameters and double-view video data; the multi-modal gait evaluation grading model module is used for processing plantar pressure distribution, lower limb joint kinematics parameters and double-view video data and outputting lower limb dyskinesia grades; and the training scheme generation model module is used for automatically generating a training scheme including a weight reduction proportion, a treadmill speed, single exercise time and training times per week according to the lower limb dyskinesia level and the personal information of the user.

The invention relates to the technical field of gait analysis, and discloses a Transform structure-based gait plantar pressure intelligent prediction system, which comprises a gait acquisition platform, a plantar pressure acquisition device, a gait kinematics acquisition device, a central processing server and a display terminal. The system obtains initial plantar pressure distribution through a lower limb multi-rigid-body chain type biomechanical model and foot-ground contact mechanics solution; and a gating prefix memory sliding window Transform is constructed in the central processing server, time sequence modeling and spatial correction are performed on the initial pressure matrix and the gait features, and a plantar pressure prediction result conforming to the biomechanical law is generated. The system effectively solves the problems that a traditional method is unstable in prediction, weak in generalization ability and lack of interpretability, plantar pressure prediction with higher precision is achieved, and the system can be used for gait evaluation, rehabilitation training and orthosis customization.

The invention relates to the field of humanoid robots, and provides a control method for biped robot step-by-step gait strategy fusion in order to solve the problems that a biped robot is large in training difficulty, unnatural in gait, poor in adaptability and the like. Training various different terrain categories through an imitation learning algorithm and a reinforcement learning algorithm to obtain a gait strategy model; s2, performing pairwise fusion gait strategy switching between the gait strategy models corresponding to various different terrain categories; s3, the terrain category of the current position is recognized in real time according to an information collection unit of the biped robot, a corresponding gait strategy model is obtained through a strategy execution control unit, and the biped robot is controlled to walk in the corresponding motion state; and S4, when the terrain category of the biped robot is changed, smooth transition between the two gait strategy models is achieved through gait strategy switching, and control over gait strategy fusion of the biped robot is completed.

The invention discloses a child gait anomaly recognition feedback method and system based on plantar pressure data, and relates to the technical field of gait anomaly recognition, and the method comprises the steps: collecting the plantar pressure data of a target wearer through a sensor array, and carrying out the feature extraction of multi-dimensional gait feature parameters; comparing and analyzing the model with a child gait development standard model, and recognizing a gait abnormal mode; carrying out risk assessment on the target shoe wearer and dividing risk levels; performing gait analysis on the target wearer to generate an individualized gait analysis report; and performing gait correction analysis on the target wearer according to the individualized gait analysis report, and matching to generate a correction training course scheme. The technical problems that in the prior art, child gait evaluation lacks quantitative standards, detection intervention is disjointed, and personalized analysis based on a dynamic development model is lacked are solved, and the technical effects of achieving objective, accurate and intelligent recognition of child gait abnormity and improving the child gait health management level are achieved.

The invention discloses a lower limb exoskeleton robot layered cooperative walking aid control method. The method comprises the steps of data acquisition and preprocessing; human-machine coupling dynamics modeling is carried out, and required moments tau < hip >, tau < knee > and tau < ankle > of the hip joint, the knee joint and the ankle joint are obtained; low-layer joint control, including hip joint moment, knee / ankle virtual impedance moment and friction compensation calculation; middle-layer multi-axis cooperation and gait phase judgment; high-rise energy is optimally distributed, assistance is distributed to three joints in a phase and a safety boundary, meanwhile, the requirements for labor saving and stability are considered, a minimization problem is written in a unified mode, J serves as the total amount, and torque of the hip joint, torque of the knee joint and torque of the ankle joint are solved in a combined mode; according to the step-by-step self-adaption of myoelectricity driving, in order to cope with human body fatigue changes, step-by-step updating is carried out on the virtual stiffness within the movement distance. The method has the advantages of improving man-machine cooperation level, man-machine adaptability and walking aid efficiency.

The application discloses a novel mobile grabbing integrated bionic multi-legged robot, which comprises a front body provided with two leg parts and a rear body provided with four leg parts; the front body and the rear body are connected through a tension structure capable of being lifted and deformed; when a driving wire is driven to be tightened, the tension structure can be lifted and deformed upward; when the driving wire is loosened, the tension structure can be lowered and restored under the action of a reset spring; the front body and the tension structure move synchronously, so that the front body can be lifted and deformed or lowered and restored. When the front body is not lifted, the robot moves in a three-legged gait through six leg part mechanisms; when the front body is lifted, two leg parts of the front body serve as a mechanical arm, a steering wheel at a hip joint is used to realize a clamping function, the robot moves through four full leg parts of the rear body, and a four-legged gait is adopted, so that the robot can complete a carrying operation.

The invention relates to the technical field of intelligent robots, in particular to a gait parameter self-adjusting method based on deep learning. The method comprises the following steps that the initial joint angle of the blind guiding robot is obtained, joint conditioning is carried out, and the joint posture deviation value is determined; attitude calibration is carried out according to the joint attitude deviation value, and attitude calibration parameters are output; starting a radar and carrying out environment perception to generate an environment map; generating point cloud height information based on the environment map; calculating a local slope by using the point cloud height information, and determining a terrain slope value; inputting the terrain slope value into a neural network model, and outputting a foot end trajectory parameter; the blind guiding robot is driven to a preset destination through the foot end track parameters and the posture calibration parameters, and the joint stability is calculated. Based on real-time joint posture calibration and terrain perception of an intelligent robot technology, self-adaptive adjustment of gait parameters of the blind guiding robot is achieved, and gait stability and navigation precision are improved.

The invention relates to a stroke gait phase recognition method and system based on a multi-stage model, and the method specifically comprises the following steps: employing a self-adaptive sliding window algorithm based on index-standard deviation fusion, carrying out the dynamic fragment interception of a gait signal of a stroke patient, and obtaining the original stroke gait data; preprocessing the original stroke gait data to obtain original gait features; constructing an integrated feature enhancement unit (IFEU), inputting original gait features, and extracting time context features of the stroke gait signals; constructing a hierarchical gait feature aggregation unit HGFAU, inputting time context features, performing gait feature extraction of each level, and obtaining multiple fusion features after fusion; and inputting the multiple fusion features into a linear layer classifier, and classifying the input features to obtain a stroke gait phase recognition result. According to the invention, gait phase recognition can be realized, and objective walking state evaluation and abnormity monitoring can be carried out on rehabilitation treatment assistance of a patient.

The invention discloses a chain type hybrid deep reinforcement learning quadruped robot control system. The system comprises a sensing module, a data processing module, a chain type hybrid reinforcement learning model module, a feedback module and the like. The invention further discloses a control method of the chain type hybrid deep reinforcement learning quadruped robot control system, and the method comprises the steps: S1, collecting bionic motion data, robot state data and environment data, and training based on an attention mechanism space-time diagram neural network and antagonistic imitation learning to obtain a reference gait model containing motion prior; and S2, constructing a three-level parallel simulation environment, setting a progressive course target, training the progressive course target by adopting an improved PPO algorithm, and optimizing the gait stability, the movement efficiency and the environmental adaptability. By means of high-precision data acquisition with the sampling frequency larger than or equal to 240 Hz and antagonistic imitation learning, motion priori knowledge is introduced, the training period can be shortened by 50% or above, abnormal gaits are avoided, and the training process is controllable.

The invention relates to the technical field of robots, in particular to a hybrid drive leg device and a humanoid robot. The leg device comprises a thigh assembly and a shank assembly. The thigh assembly comprises a first driving mechanism, a second driving mechanism, a thread shifting device and a pair of first structural plates. The first driving mechanism comprises a first motor, a first conical wheel, a first guide wheel and a first rope. The first motor drives the first conical wheel to rotate and drives the first structural plate to swing. The shank assembly comprises a pair of second structural plates, and the second driving mechanism is used for driving the second structural plates to swing. The thread shifting device drives the first rope to move in the axial direction of the first conical wheel and drives the second rope to move in the axial direction of the second conical wheel, so that the leg device can stably operate under the conditions of different gaits and loads. In addition, the leg device of the scheme comprises the elastic buffer piece and the hydraulic damper, the elastic buffer piece and the hydraulic damper can absorb impact force borne by the leg device in real time in the movement or landing impact process of the leg device, and the overall stability of the leg device is improved.