24 results about "Gait pattern" patented technology

The invention discloses a complex terrain-oriented quadruped robot self-adaptive gait generation system and method. The system comprises a sensor system used for collecting terrain data and robot state information; the terrain sensing module identifies terrain types and terrain feature parameters according to sensor data, and quantifies terrain features into parameters; the multi-modal gait library is used for storing a plurality of basic gait modes and corresponding gait parameters; the parameterized gait generation module is used for selecting a basic gait from the gait library and optimizing and generating an optimal gait adaptive to the current environment according to terrain parameters and task parameters; the gait smooth transition algorithm synthesizes all intermediate postures in the time and space interpolation generation transition process to obtain a smooth transition trajectory; the terrain adaptability adjusting module is used for finely adjusting the gait parameters according to real-time feedback; and the execution control module is used for converting the generated gait parameters into joint control instructions for controlling the robot so as to execute corresponding actions. The quadruped robot can adapt to more complex environments.

The invention relates to the technical field of intelligent control, and discloses a hip joint exoskeleton control method and system based on gait mode prediction and gait phase estimation, a terminal and a storage medium, and the method comprises the steps: constructing and training an initial unsupervised terrain detection model, and obtaining a target unsupervised terrain detection model; inputting the environment binary image into a target unsupervised terrain detection model to obtain a terrain detection result, and obtaining a gait mode prediction result based on a majority voting strategy; according to the hip joint angular velocity, target gait phase estimation is obtained based on a self-adaptive oscillator system; on the basis of the expected auxiliary torque curve, net auxiliary torque is obtained according to the gait mode prediction result and target gait phase estimation, and hip joint exoskeleton assistance modes are switched according to the net auxiliary torque. According to the method, marking of a large amount of data is avoided through the unsupervised terrain detection model, terrain detection and exoskeleton control are combined, and the terrain self-adaptive walking capacity of the exoskeleton is enhanced.

The application relates to the technical field of intelligent control, and discloses a hip exoskeleton control method and system based on gait pattern prediction and gait phase estimation, a terminal and a storage medium, the method comprising the following steps: constructing an initial unsupervised terrain detection model and training the model to obtain a target unsupervised terrain detection model; inputting an environment binary image into the target unsupervised terrain detection model to obtain a terrain detection result, and obtaining a gait pattern prediction result based on a majority voting strategy; obtaining a target gait phase estimation based on an adaptive oscillator system according to a hip joint angular velocity; obtaining a net auxiliary torque based on an expected auxiliary torque curve, a gait pattern prediction result and the target gait phase estimation, and switching a hip exoskeleton assistance mode according to the net auxiliary torque. The unsupervised terrain detection model avoids labeling a large amount of data, the terrain detection is combined with the exoskeleton control, and the terrain self-adaptive walking capability of the exoskeleton is enhanced.

The invention discloses an exoskeleton driving method based on real moment constraint and continuous gait phase modeling, which comprises the following steps: acquiring multi-modal signal data in the gait process of a subject by using a multi-sensor synchronous acquisition system, including IMU signals, EMG signals, plantar pressure signals and optical motion capture data; preprocessing the multi-mode signal data to obtain a preprocessed signal; performing continuous gait phase prediction and gait pattern recognition according to the preprocessed signal; estimating a joint instantaneous moment by using a pre-trained moment estimation network based on the predicted gait phase and gait mode; and converting the obtained instantaneous moment of the joint into a driving signal of an exoskeleton actuator. According to the method, discrete event triggering is replaced by continuous gait phase modeling, so that the smoothness and robustness of control are ensured; meanwhile, a mechanical real constraint and individualized moment estimation mechanism is introduced, stable man-machine cooperative control is achieved, and the adaptability and use experience of the exoskeleton in a complex environment are remarkably improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.

Described herein are methods, systems and computer-program products for intra- and inter-gait classification for both symmetric and asymmetric gait patterns using images.

The control device (10) according to this disclosure switches parameters that affect the ground reaction force at the feet of the robot's (20) legs (22) in response to a change in the robot's (20) gait pattern, and outputs control information used to control the legs (22) based on the parameters.

The invention discloses a gait recognition method for a Parkinson's disease patient, a medium and equipment, and the method comprises the steps: collecting gait video data of a user, inputting a trained gait recognition model, and outputting a Parkinson's disease classification result. According to the model, space-time gait features are extracted by adopting a backbone network, and the focusing capability on PD typical gait features is enhanced in combination with a multi-scale attention mechanism module; according to the method, time sequence dimensions are compressed through a horizontal pooling module, feature normalization and classification optimization are carried out through a BNNeck module, and triple loss and cross entropy loss combined training is adopted so as to improve the discrimination capability of the model on a PD gait mode. And finally, a fine-grained identity recognition result is converted into binary classification output of Parkinson's disease patients and non-patients through a secondary discriminator. According to the method, automatic detection of PD gait abnormity is realized, the recognition precision of the model on a PD specific motion mode is effectively improved, and early screening and auxiliary diagnosis of Parkinson's disease are facilitated.

The application discloses an interpretable abnormal gait detection method and model based on multi-task graph fusion learning, and the method is: constructing a gait graph and arranging multiple gait graphs according to time to obtain a time sequence gait graph; constructing an LRP-MGG neural network model; extracting gait time dependence features through the cumulative GRU block of the LRP-MGG neural network model; obtaining local space features embedded in the time dependence feature space by using a deep GCN module; performing graph-based gait mode classification through a DiffPool module; learning interpretable abnormal gait information through a CNN deep convolutional neural network model by using gait space-time significant difference features; and calculating the importance score of each feature by using a hierarchical relevance propagation LRP. The application combines the training of multiple tasks, reduces the amount and complexity of data, and thus improves the generalization ability and prediction ability of the model.

A walker device is described herein including two side frames, each side frame including a front vertical support, a rear vertical support and at least one connecting member extending between the front and rear vertical supports. The walker device further includes a cross bar extending between the two side frames and pivotally connecting to the front vertical supports of the two side frames. The walker device also includes a tension device which applies a resistive force to the pivoting motion between the first cross bar and the two side frames. The walker device is therefore capable of a pivoting configuration which enables the user to learn or re-learn a more natural gait pattern including rotation of the upper and lower body. Some embodiments of the walker device include a second, auxiliary cross bar that is parallel to the first cross bar and horizontally offset from the first cross bar.

The present application relates to a fatigue gait pattern recognition device and method based on a multi-modal sensor, multi-modal sensor data is collected based on a multi-modal sensor, gait segmentation is performed after preprocessing, data samples after gait segmentation are obtained, including multi-modal sensor data and corresponding data features; an improved feature fusion model is constructed, and the model is trained to be stable with data samples; multi-modal sensor data is collected, and the preprocessed data is input into the trained model, and the fatigue gait pattern recognition result is output; the device includes two groups of multi-modal sensors for collecting multi-modal sensor data, and the synchronous transceiver device sends the corresponding two groups of multi-modal sensor data after matching; the controller acquires the multi-modal sensor data and recognizes the fatigue gait pattern. The present application realizes high-precision classification of fatigue gait pattern, and the accuracy is improved by 15%-20% compared with traditional single-mode method; based on dynamic threshold classification and multi-dimensional feature analysis, user-specific rehabilitation suggestions are generated, and walking ability and safety are improved.

The invention provides a method and a device for evaluating active substances to relieve chronic alcoholic brain injury. The method comprises the following steps: acquiring gait data of a normal group, a model group and an intervention group; carrying out preprocessing and feature extraction on the collected original gait data; training the gait data of the normal group and the model group, and establishing gait mode models in normal and injured states; and inputting the gait data of the intervention group into the gait mode model for similarity scoring, and evaluating the relieving effect of the active substances on the chronic alcoholic brain injury according to the scoring result. According to the method, the limitation of traditional single parameter comparison is broken through, and the improvement condition of active substances on the gait of the mouse with the chronic alcoholic brain injury is reflected more comprehensively and accurately on the whole; rapid and batch evaluation of various active substances and different doses is realized, and the drug research and development efficiency and the active substance screening efficiency are remarkably improved; the subjectivity in the evaluation process is reduced, and the reliability and comparability of the evaluation result are improved.

This system provides a gait analysis system that easily analyzes gait patterns from walking videos taken with smartphones, etc., and automatically generates a natural language report that can be understood without requiring specialized knowledge. [Solution] The gait analysis system of this invention consists of a camera device that films a person walking, a gait analysis device that analyzes the walking state using the video from the camera device, and an output device that automatically generates and outputs a natural language report based on the analysis results of the gait analysis device. Skeletal points are estimated from the video, and the left and right support phases are extracted to calculate gait indicators such as trunk sway, joint inclination, knee sway, and step width. Multiple viewpoints, including frontal and lateral views, are analyzed separately, and the video quality is evaluated using the visibility of skeletal points, the number of frames, and the continuity of joint coordinates. The obtained indicators are compiled as structured data, and the characteristics of each left and right support phase and walking tendencies are expressed in text using artificial intelligence and output, thereby enabling simple and reliable gait evaluation.

The application discloses a gait analysis method and system based on an IMU and a kinematic constraint convolutional neural network, and relates to the technical field of gait biomechanics analysis. The method is characterized in that IMU sensors are arranged at the positions of human feet, shanks, thighs and sacrum of a trunk, a convolutional neural network is constructed, distances from the sensors to joint axes and limb segment centroid positioning are adaptively estimated in combination with static human body parameters of a subject, then gait energy estimation is performed to realize gait analysis, and gait modes such as normal walking, obstacle crossing, walking on a sandy ground, going upstairs and going downstairs can be distinguished through coupling relationships among different energies. The gait analysis method and system based on the IMU and the kinematic constraint convolutional neural network can reduce system errors caused by position deviation of sensor wearing, is suitable for rehabilitation evaluation, motion function monitoring, auxiliary identification of abnormal gait and continuous gait energy analysis in a daily environment, and does not need to depend on an optical motion capture system and a force platform.

To provide an information analysis device that can identify environmental factors that affect gait. [Solution] The information analysis device comprises: a collection unit that collects gait information showing the measurement results of the gait patterns of multiple pedestrians in a target area; a detection unit that detects the location of periodic disturbances in the time-series data of sensor data included in the gait information; an identification unit that identifies the environmental factors at the location of periodic disturbances in the time-series data of sensor data; and an output unit that outputs information regarding the identified environmental factors.

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.

Sports shoe system (1) with integrated sensors, comprising a sensor insole (10) with piezoelectric pressure sensors (21-28) for measuring the pressure distribution under the foot and with bioimpedance electrodes (11-14) for measuring the electrical body impedance, characterized in that the sensor insole (10) and an electronic module (30) connected to it are jointly designed to calculate body weight from the pressure sensor signals (21-28), to determine body composition from the bioimpedance signals (11-14), to perform a three-dimensional reconstruction (60) of the foot position and gait pattern and to automatically generate personalized recommendations for orthopedic insoles (70) from this, wherein all measurements are performed simultaneously during sporting activity.

This invention relates to the field of robot adaptive control technology, and provides a method for terrain processing and multi-terrain gait control of a humanoid robot, comprising: planning an inspection route and segmenting walking segments according to spatial location and functional area; acquiring multi-source data using a multi-source environmental perception unit, fusing each walking segment, and extracting terrain features; mapping the terrain features of each walking segment to terrain labels with road surface type identification and safety risk level according to preset discrimination rules, and converting the safety risk level into a gait level; automatically selecting a target gait mode according to the gait level, and issuing gait control parameters matching the gait mode to each joint controller; monitoring the foot contact state and posture changes in real time, comparing them with the terrain label and gait level of the corresponding walking segment, dynamically adjusting the gait level of the corresponding walking segment and its subsequent segments, and updating the gait control parameters.

A portable device for monitoring a person's gait is provided, comprising a distance sensor configured to measure the distance between the foot and the floor during gait; and a processing module configured to determine, based on the measured distance, whether there is a deviation of the gait from a predetermined gait pattern; as well as a system comprising the portable device, whose processing module is configured to transmit a notification of the deviation of the gait to an external device, and the external device.

The invention discloses a modular self-adaptive lower limb rehabilitation exoskeleton robot system based on artificial intelligence, and belongs to the technical field of medical rehabilitation equipment. Comprising a modular mechanical structure which supports the lower limbs of a user in a wearable manner and comprises detachable hip joint, knee joint and ankle joint modules; the multi-modal sensor subsystem is used for collecting lower limb kinematics, dynamics and neuromuscular signals of a user in real time and carrying out data fusion; the artificial intelligence control subsystem comprises a processing unit and a memory; the processing unit is configured to identify a motion intention and a gait pattern of the user through a machine learning model based on the fused data; and a reinforcement learning algorithm is used, auxiliary torque applied to the corresponding joint module is dynamically adjusted according to the recognition result, and self-adaptive assistance is achieved. The rehabilitation effect and efficiency can be improved, the patient participation degree and motivation are enhanced, human-computer interaction safety is guaranteed, full-cycle rehabilitation coverage is achieved, and technical accessibility is improved.

The invention provides a gait recognition method and device based on fusion of myoelectricity and inertial data, and the method comprises the steps: obtaining a myoelectricity original signal flow collected by a myoelectricity sensor and an inertial original signal flow collected by an inertial sensor, obtaining a synchronized myoelectricity-inertial data flow, carrying out the detection of a gait event trigger point, and carrying out the recognition of the gait. Identifying a heel landing event point and a tiptoe off-ground event point in the gait cycle, segmenting the synchronized myoelectricity-inertia data stream according to the heel landing event point and the tiptoe off-ground event point, generating a gait cycle unit set, performing envelope feature extraction and attitude angle change sequence extraction on each gait cycle unit in the gait cycle unit set, and obtaining a gait cycle unit set; carrying out correlation analysis to obtain a myoelectricity-inertia coupling activation mode descriptor, carrying out phase transition path reasoning on the myoelectricity-inertia coupling activation mode descriptor to obtain a phase state transition path topology, carrying out structural similarity calculation on the phase state transition path topology and a plurality of reference path topologies in a preset gait mode library, selecting a gait category label, determining a phase offset, and outputting a gait recognition result. According to the invention, the accuracy and fineness of gait recognition are improved.

The invention discloses a data-driven intelligent gait health detection and positioning system solution, and belongs to the technical field of wearable rehabilitation equipment. The system comprises a three-point flexible film pressure sensor array, an IMU (inertial measurement unit), a GPS (global positioning system) positioning module, a master-slave distributed control platform and a communication module, wherein sensor data is acquired by the slave control board and then transmitted to the main control board, and the main control board realizes gait pattern recognition, fall risk early warning and user positioning based on multi-sensor fusion and a machine learning algorithm; the system adopts a hierarchical processing strategy, a first-level model is deployed on a main control board to realize real-time state recognition and low-power-consumption control, and a second-level model is deployed on a cloud to realize gait type subdivision and health assessment. According to the invention, through multi-source data fusion and edge cloud cooperative computing, the gait recognition precision and the timeliness of fall early warning are improved, the wearing comfort and the system endurance are considered at the same time, and the method is suitable for daily health monitoring of old people, rehabilitation patients and other groups.

The invention relates to an exoskeleton admittance control method based on reinforcement learning, and the method comprises the steps: collecting the limb movement posture of a user and human-computer interaction force information through a sensor, and carrying out the preprocessing; the processed data are input into a reinforcement learning model which is trained offline in advance, the model is based on a near-end strategy optimization algorithm and comprises a strategy network and an evaluation network, the strategy network is used for recognizing gait modes and phases and generating expected knee joint angle curves and optimal admittance control parameters in a self-adaptive mode, and the evaluation network is used for evaluating the optimal admittance control parameters through a performance reward function. The sub-targets include gait recognition accuracy, exoskeleton power consumption, human body fatigue and the like, parameters are evaluated, and a strategy is optimized; the optimal parameters are loaded to a variable admittance controller, expected assistance torque is calculated, and a power module is driven to output; and finally, feeding back the actual power-assisted torque in real time through a torque sensor, and performing closed-loop correction. The power assisting device can adapt to different motion states such as walking and stair climbing, efficient and low-power-consumption personalized power assisting is achieved, and the user experience is improved.