1276 results about "Exoskeleton" patented technology

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a man-machine exoskeleton cooperation method based on multi-modal signal fusion, and relates to the technical field of exoskeleton program control. The method comprises the following steps that man-machine exoskeleton driving pre-pressure cold and hot mutation analysis is conducted in combination with man-machine exoskeleton man-machine cooperation components; and performing memory alloy spring pre-pressure optimization adjustment in combination with a man-machine exoskeleton driving pre-pressure cold and hot mutation analysis result. According to the invention, cold chain exoskeleton cooperative control is realized through multi-mode signal fusion, and three-level regulation and control are implemented based on a current threshold value and a myoelectricity state: graded current rising / falling and safety early warning are carried out in case of abnormal current; dynamic gain compensation or joint retardation protection is carried out when myoelectricity is abnormal; according to the man-machine exoskeleton cooperative control method and the man-machine exoskeleton cooperative control system, the effect of improving the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is achieved, and the problem that the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is disabled in the prior art is solved.

The invention discloses a lower limb exoskeleton gait track prediction method based on an LSTM-KAN fusion model, and the method comprises the steps: collecting human motion data through a sensor assembly, and carrying out the filtering, missing value processing and normalization of the human motion data; then constructing an overall architecture of a prediction model based on an LSTM-KAN network, optimizing parameters of the prediction model by using a particle swarm optimization (PSO) algorithm, extracting key features in the preprocessed data as a training data set, and inputting the training data set into the prediction model for training; and finally, collecting current human body motion data, pre-processing the current human body motion data, inputting the pre-processed current human body motion data into the trained prediction model, and outputting future human body gaits and tracks by the prediction model. And the controller takes a future gait track generated by the prediction model as a reference track to generate a driving signal and sends the driving signal to the actuator so as to realize accurate control of the actuator. By adopting the gyroscope sensor, the acceleration sensor and the pressure sensor for gait estimation, exoskeleton motion control can be effectively improved, and man-machine interaction experience is effectively improved.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention discloses a brain-computer interface upper limb motion recovery training system, and relates to the technical field of upper limb motion recovery training, the brain-computer interface upper limb motion recovery training system comprises a multi-modal neural signal acquisition module, a combined regulation and control module and a rehabilitation exoskeleton module, the multi-modal neural signal acquisition module is connected with the combined regulation and control module through a wireless Bluetooth protocol, and the combined regulation and control module is connected with the rehabilitation exoskeleton module. The combined regulation and control module is connected with the rehabilitation exoskeleton module through a CAN bus, and the combined regulation and control module is used for synchronously processing fNIRS blood flow signals, tDCS nerve regulation and control parameters and EEG electrophysiological characteristics and generating a closed-loop feedback instruction. By installing the signal fusion unit and the intention analysis unit, time-space complementary fusion of EEG and fNIRS signals is realized, the problem of low precision of traditional single-mode intention recognition is solved, and the motion intention decoding accuracy is improved.

The invention discloses an intelligent wearable exoskeleton self-adaptive auxiliary regulation and control method and an intelligent wearable exoskeleton system.The intelligent wearable exoskeleton self-adaptive auxiliary regulation and control method comprises the steps that a motion data sample of human walking is obtained, and a human motion model is constructed based on a man-machine closed-chain motion model according to the motion data sample; collecting multi-modal data of the target user when the target user wears the exoskeleton in real time; calling a human body motion model and a man-machine closed-chain motion model, and generating an exoskeleton control instruction of man-machine motion cooperation according to the multi-modal data; and dynamically adjusting motion control parameters of the exoskeleton according to the control instruction. According to the method, the precise human body motion model is constructed based on the man-machine closed-chain motion model, the man-machine motion cooperative exoskeleton control instruction is generated in combination with the model, the exoskeleton motion control parameters are dynamically adjusted according to the instruction, the body type difference, the muscle strength level and the motion habit of different users can be precisely matched, and the wearing comfort and safety are effectively improved.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The embodiment of the invention relates to the technical field of information, and discloses an intelligent data acquisition method and device, a medium and a product, the intelligent data acquisition method comprises the following steps: acquiring artificial demonstration data through an exoskeleton system, the artificial demonstration data comprising video stream data, motion data and force sense data; importing the video stream data into a first large language-vision model, segmenting the video stream data into a plurality of independent task fragments, and generating independent task fragment descriptions; importing the independent task fragment description into a large language model, and generating a task label based on natural language description; and exporting the independent task segments, the task labels and the corresponding motion data and force sense data to generate intelligent data. Therefore, a low-cost, high-efficiency and large-scale intelligent data acquisition and processing method is established, and automatic conversion from human operation data to high-quality VLA training data is realized.

Provided in the present invention are an exoskeleton shared control method, system and device based on AI co-driving, and a storage medium. The exoskeleton shared control method based on AI co-driving comprises: acquiring real-time pose data of an exoskeleton wearer; on the basis of the pose data, performing switching between a local trajectory correction mode and a hybrid long-short-term motion optimization mode in real time, wherein the local trajectory correction mode is completed on the basis of an AI co-driving policy network model; and performing multi-task tracking on the direction of a three-dimensional motion of an exoskeleton and a walking trajectory thereof, so as to achieve motion tracking control over the exoskeleton. By using the method provided in the present application, when encountering a sudden obstacle, an initial path can be corrected to an optimal path in combination with pose data and target point image feature data, thereby helping an exoskeleton to realize omnidirectional walking in a three-dimensional complex environment.

The invention discloses a control method and system of a lower limb exoskeleton power assisting device for iron tower climbing. The method comprises the steps that electromyographic signals are processed, and muscle activation degree indexes are calculated; the two-channel LSTM neural network realizes motion intention online prediction; iron tower cross arm structure edge identification and safe climbing path generation; designing an asymmetric impedance function and optimizing joint stiffness damping parameters; solving a metabolic consumption evaluation model and a minimum energy consumption assistance curve; and real-time calculation is realized through three-stage control period setting and hardware acceleration. Based on the method, all the modules of the exoskeleton power assisting device operate cooperatively; data are accurately collected through the sensor, the power and execution module is flexibly adjusted according to instructions, efficient operation is ensured through the control and operation module, stable electric power is provided through the energy module, and therefore the safety and efficiency of iron tower climbing operation are remarkably improved, and the burden of operators is relieved.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to the technical field of wearable exoskeleton data acquisition and processing, and discloses a wearable exoskeleton data acquisition and processing method and system based on digital twinning, and the method comprises the steps: building a simulation mapping model of exoskeleton joints through employing the digital twinning technology according to upper limb motion data; according to the upper limb motion data, executing a kinematics modeling strategy, calculating an actual pose of the end effector by using a forward kinematics algorithm, and substituting a target pose into inverse kinematics for solving, specifically, obtaining a joint angle, executing a Jacobian matrix modeling strategy, and establishing a model between the actual pose and the joint angle; the influence of the variable quantity of the joint angle on the actual pose of the end effector is obtained; if the pose error is smaller than or equal to an error threshold value, acquiring a joint angle at the moment, and recording the joint angle as a target joint angle; if the pose error is greater than an error threshold value, executing a joint angle tuning strategy, adjusting the joint angle, and repeating the steps S2-S5; the simulation precision is improved, and the man-machine coordination is improved.

The invention discloses a double-clutch variable-stiffness knee joint exoskeleton, and relates to the technical field of wearable lower limb exoskeletons.The double-clutch variable-stiffness knee joint exoskeleton comprises a gait sensing module arranged outside and a limb connecting arm module used for being connected with a human body, and an energy storage module is installed on one side of the limb connecting arm module; a sensor module is mounted on the other side of the limb connecting arm module; the limb connecting arm module comprises a limb connecting arm assembly I used for being connected with the thigh of the human body and a limb connecting arm assembly II used for being connected with the shank of the human body, the limb connecting arm assembly I and the limb connecting arm assembly II are each provided with a clutch unit, and the core speed increaser is installed between the limb connecting arm assembly I and the limb connecting arm assembly II. The energy storage module and the sensor module are arranged on the two sides of the core speed increaser, and the core speed increaser is connected with the energy storage module. The device has the advantages of compact structure, variable rigidity bandwidth and good motion adaptability, can realize high energy recovery efficiency in a multi-gait mode, and reduces the metabolic consumption of a human body.

The utility model discloses an outer skeleton, prefilter and water consuming system relates to prefilter technical field, wherein outer skeleton includes barrel frame and siphon runner, said siphon runner includes the side wall runner of said side frame and is provided the bottom runner of said chassis, said side wall runner is communicated with said bottom runner, said bottom runner is provided with said bottom runner, said side wall runner is provided with said side frame, said bottom runner is provided with said bottom runner, said bottom runner is provided with said bottom runner, said bottom runner is provided with said bottom runner. The side wall flow channel is provided with a siphon hole communicated with the inner cavity of the cylinder frame, and the siphon flow channel is provided with a first discharge port communicated with the outside of the cylinder frame. According to the scheme, the rotatable outer framework and the siphon flow channel are introduced, so that the filtering efficiency and the cleaning effect are improved, the maintenance process is simplified, and more convenient, efficient and economical use experience is brought to a user.

The invention discloses an exoskeleton attitude prediction system for building construction, particularly relates to the field of attitude prediction, and comprises a multi-dimensional sensing module, a data preprocessing module, an attitude prediction model module and a control decision module. A multi-dimensional sensing module collects joint movement, muscle movement and human-computer interaction force data through a nine-axis IMU, a surface myoelectricity sensor and a miniature strain type force sensor; the preprocessing module obtains standardized feature data through filtering, standardization and exception elimination; the attitude prediction model uses a CNN-LSTM hybrid architecture, and outputs a target attitude of 0.5-2 seconds; and the control decision module compares a prediction result with a safety threshold value, generates control instructions in three levels to drive exoskeleton correction, synchronous feedback and recording, solves the problem of response delay of a traditional exoskeleton, and improves the construction safety and efficiency.

The invention relates to the field of rehabilitation instruments, in particular to an intelligent pneumatic hand function rehabilitation training system which comprises a flexible exoskeleton glove provided with a pneumatic muscle array and used for assisting fingers of a user in grasping training. The fingertip position of the glove is connected with a magnetorheological damper through a flexible cable, and the magnetorheological damper is used for providing dynamic resistance in the training process. The electrical stimulation module is used for activating hand muscle groups in a targeted manner through a surface electrode and enhancing neuromuscular control; the sensor module is used for collecting physiological data of a user in real time; the physiological data comprises electromyographic signals and tissue blood oxygen saturation; the control unit is used for switching training modes, by means of the intelligent air pressure hand function rehabilitation training system, the training intensity can be matched with the real-time ability of a patient all the time, and the rehabilitation training effect is improved.

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.

An exoskeleton system comprising a leg actuator unit that is configured to be coupled to a leg of a user. The leg actuator unit includes: an upper arm and a lower arm that are rotatably coupled via a rotatable joint, the rotatable joint configured to be positioned at a knee of the user with the upper arm coupled about an upper-leg portion of the user above the knee and with the lower arm coupled about a lower-leg portion of the user below the knee. The upper arm is configured to be coupled to the upper-leg portion above the knee via a first set of couplers that includes a first upper-leg coupler, the lower arm is configured to be coupled to the lower-leg portion below the knee via a second set of couplers that includes one or more lower-leg couplers associated with a lower-leg brace, and an actuator extends between the upper arm and lower arm, the actuator configurable to move the upper arm and lower arm.

The invention relates to the technical field of wearable exoskeleton assistance, in particular to a lower limb exoskeleton knee joint assistance control method and system. The lower limb exoskeleton comprises a pair of swing arms respectively connected with thighs and shanks and an actuator, and the method comprises the following steps: processing data of a motion parameter sensor, acquiring detection data input by the motion parameter sensor arranged on the lower limb exoskeleton, and processing the detection data to extract motion features; gait phase detection is conducted, the current gait phase is determined according to the motion characteristics, the gait phase comprises a supporting phase and a swinging phase, the supporting phase corresponds to the state that the lower limb touches the ground, and the swinging phase corresponds to the state that the lower limb leaves the ground; activity identification: motion features are extracted in the supporting phase to determine activity types, and the activity types comprise at least one of flat walking, upward walking, downward walking, running, squatting, backward walking and unknown; according to the control strategy, the corresponding control strategy is selected according to the gait phase and the activity type so as to control an actuator.

The invention relates to the field of upper limb joint rehabilitation training, in particular to an upper limb joint rehabilitation training method and system based on an exoskeleton mechanical arm, and the method comprises the steps: collecting sEMG signals of a target muscle group of a user in real time in a training process; calculating the root mean square of the amplitude of the sEMG signal in the current time window; if the root mean square is smaller than a muscle force insufficient threshold value, a training mode is switched to a full-assistance mode; if the root mean square is not smaller than the muscle force insufficient threshold value and smaller than the normal muscle force threshold value, the training mode is switched to a semi-assistance mode; if the root mean square is larger than or equal to the normal muscle force threshold value, the training mode is switched to an anti-resistance training mode. The rehabilitation training effect of the upper limb joints can be effectively improved.

The invention discloses a lower limb exoskeleton control system and method based on phase recognition and model prediction control. The system comprises a kinematics modeling module, a gait database module, a data processing and feature extraction module, a gait phase recognition module, a dynamics modeling module, a model prediction controller and an actuator control unit. By recognizing the gait phase and dynamically adjusting and controlling the target, precise assistance of the natural gait of the wearer is achieved. The system calculates the trajectory of the ankle joint relative to the hip joint in real time based on a kinematic model, and constructs a database in combination with gait data collected under multiple working conditions to provide an expected trajectory and reference parameters for control. In the dynamic modeling process, motor driving torque and man-machine interaction torque are introduced, a system dynamic model is established in a unified mode, and control characteristics under different gait phases are reflected.

The present application relates to the field of medical devices, and provides a back plate adjustable exoskeleton, comprising: a column structure and a control module, the column structure is slidably connected with a back plate mechanism, and a lower limb exoskeleton connected with the back plate mechanism, and the back plate mechanism is provided with a back plate which can move up and down relative to the back plate mechanism; further comprising: a binding device arranged on the lower limb exoskeleton and a foot bottom exoskeleton, the foot bottom exoskeleton is provided with a pressure sensor. When the human body wears the exoskeleton and changes from a sitting posture to a standing posture, the back plate mechanism adjusts the back plate mechanism along the column structure to the height matched with the user according to different body shapes of the user; and when the user changes the posture in the walking process in the standing posture, the foot bottom exoskeleton detects the change of the foot bottom pressure, so that the control module can control the up and down movement of the back plate relative to the back plate mechanism according to the foot bottom pressure to adjust the human body posture.

An exoskeleton system comprising at least one leg actuator unit configured to be coupled to leg of a user, the leg actuator unit including: an upper arm and a lower arm that are rotatably coupled via a joint, the joint positioned at a knee of the user with the upper arm coupled about an upper leg portion of the user above the knee and with the lower arm coupled about a lower leg portion of the user below the knee, a leg-actuator-unit user interface comprising a plurality of input and feedback elements, and an actuator that extends between the upper arm and lower arms.

The invention discloses a passive power-assisted exoskeleton, and belongs to the technical field of auxiliary walking appliances, the passive power-assisted exoskeleton comprises a leg assembly, the leg assembly comprises a thigh supporting assembly, a shank supporting assembly, a crossed four-bar linkage assembly and an energy storage power-assisted assembly, the thigh supporting assembly is in power connection with the shank supporting assembly through the crossed four-bar linkage assembly, and the energy storage power-assisted assembly is in power connection with the energy storage power-assisted assembly; the energy storage assisting assembly comprises an elastic energy storage assembly, a brake cable, a guide wheel set and a cable length adjusting device. Passive power assisting is achieved through the energy storage power assisting assembly, the leg load is reduced, the wearing comfort and the feasibility of long-time walking are improved, the crossed four-connecting-rod assembly enables the motion trail of the exoskeleton to better conform to the natural motion trail of the knee joint of the human body, and the motion trail is more stable. Through the elastic energy storage assembly, the cable length adjusting device and the guide wheel set, the adjusting precision of the tensioning degree of the brake cable is improved, the influence of knee joint bending on the length of the brake cable is amplified, the compression degree of an elastic piece and the control precision of stored energy are improved, and the load-bearing auxiliary effect is greatly improved.

The invention discloses an ankle joint mixed exoskeleton system fused with functional electrical stimulation. The ankle joint mixed exoskeleton system comprises a leg support used for being fixed to a shank, a foot fixing plate hinged to the bottom of the leg support, a driving mechanism arranged on the leg support and used for driving the foot fixing plate to swing and a system module matched with the driving mechanism. The invention further provides an assisting torque distribution method. The system provided by the invention can effectively assist the patient in keeping the correct position of the foot in the walking process, so that the foot drop is effectively prevented, and the walking safety and efficiency are improved.

The invention provides an exoskeleton system assistance control method and device based on a man-machine co-fusion strategy, and the method comprises the steps: collecting basic motion data of a human body and an exoskeleton system through various types of equipment, and initializing the motion state of the exoskeleton system; constructing a man-machine co-fusion kinetic model based on the basic motion data; on the basis of a man-machine co-fusion kinetic model, processing the collected real-time data through an EKF algorithm, and estimating various dynamic parameters of a human body and an exoskeleton system; determining a power-assisted control strategy of the exoskeleton system in combination with the dynamic parameters and a man-machine co-fusion kinetic model, and adjusting the power-assisted control strategy according to the personalized parameters of the user; and controlling the exoskeleton system to execute the adjusted power-assisted control strategy. According to the method, the man-machine collaborative motion intention prediction algorithm is adopted for power assisting control, the power assisting accuracy and flexibility of the exoskeleton system can be improved, and the method can dynamically adapt to individual differences of users.

The invention belongs to the technical field of wearable exoskeletons, and particularly relates to a forearm power assisting device, a forearm power assisting method and an upper limb power assisting system. The forearm power assisting device comprises an angle transmission mechanism, an angle amplification mechanism, an elastic energy storage mechanism and a tension output mechanism; wherein the buckling angle of the front arm is transmitted through the angle transmission mechanism, and after the buckling angle is amplified through the angle amplification mechanism, the elastic energy storage mechanism is driven, so that the flexible traction cable outputs pulling force linked with the buckling angle of the front arm. The forearm power assisting device is a system based on the pure mechanical transmission and energy storage principle, forearm flexion and extension angle changes can be directly converted into self-adaptive tension linked with the forearm flexion and extension angle changes, and therefore forearm muscle fatigue in long-time fine operation can be effectively relieved without an external power source.

The invention belongs to the technical field of robots, and relates to a single-motor transverse walking exoskeleton based on a gear transmission system, the single-motor transverse walking exoskeleton comprises a waist assembly, an electric control assembly, a swing assembly and a thigh binding assembly, the waist assembly is used for being bound to the waist of a wearer; the electric control assembly is mounted on the waist assembly, comprises a motor, a synchronous belt, a synchronous wheel, a clutch and a gear set, and is used for providing power; the swing assembly is connected with the electric control assembly and used for driving the legs of the wearer to swing transversely. The thigh binding assembly is connected with the swing assembly and used for being bound to the leg of a wearer. The motor drives the synchronous wheels on the two sides through the synchronous belt, transmission of the gear set is controlled through the clutch, and transverse walking assistance or resistance control over the legs on the two sides through the single motor is achieved. According to the single-motor transverse walking exoskeleton based on the gear transmission system, transverse walking assistance which is light in weight, low in cost and high in controllability is achieved through the design of combination of the single motor, the double clutches and the gears.

The invention relates to a virtual hand grasping perception and exoskeleton force feedback mapping method based on virtual-real interaction, and belongs to the technical field of virtual reality, the method comprises the following steps: building a hand exoskeleton, and reading joint data in real time; calculating the pose of each joint coordinate system of the virtual hand relative to the virtual world; according to the calculated poses of all the joints of the virtual hand, the virtual hand is driven to synchronously move with the hand exoskeleton in real time; collision and contact events of the virtual hand and the digital object are monitored in real time, and virtual interaction force generated when the virtual hand interacts with the digital object is calculated; calculating feedback torque required to be output by each hand exoskeleton motor of the hand exoskeleton, and driving the motors to output corresponding feedback force through a motor controller of the hand exoskeleton, so as to realize force feedback of the hand. According to the method, the interaction reality sense in the virtual reality application can be enhanced, the user can interact with the virtual environment more naturally in an immersive mode, and the user experience is improved.

The invention discloses a quasi-passive knee joint exoskeleton and a design method thereof, the knee joint exoskeleton comprises a transmission energy storage module, a power-assisted regulation and control module and a wearing module, and the power-assisted state is switched according to the gait stage of a human body. The transmission energy storage module transmits joint torque through an input gear, an intermediate gear and an output gear, and a pressing rod on an output shaft is used for driving a flexible hinge to deform, so that efficient energy storage and release in a small space are realized; the assistance regulation and control module controls a cam shaft to rotate, drives a swing shaft to push an intermediate gear to move and enables the gears to be meshed in the supporting phase, motion limiting is formed through contour matching of a cam shaft body and a swing shaft matching cambered surface, mechanical self-locking is achieved, locking force does not need to be additionally provided, and walking assistance is achieved. In the swing phase, the camshaft is controlled to rotate to enable the unlocking cambered surface to turn to the swing shaft, the swing shaft is unlocked, a driving pin of the camshaft drives the swing shaft to rotate, the gear is disengaged, and free swing is achieved. Compared with the prior art, the invention has the advantages of light weight and low power consumption.