668 results about "Biomechanics" 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 relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of medical equipment control, in particular to a control system and a control method of cornea crosslinking equipment. The control system comprises a multi-modal data fusion module which is configured to integrate a preoperative corneal topographic map curvature matrix, OCT layering thickness data and biomechanical parameters to generate a four-channel fusion tensor; the virtual cornea modeling module is configured to input a conditional generative adversarial network to generate a virtual cornea model under the condition of the four-channel fusion tensor; the dynamic energy planning module is configured to generate a thermodynamic diagram of energy distribution based on mechanical response prediction of the virtual cornea model; and an illumination control module configured to perform patterned illumination based on the thermodynamic diagram. According to the method, the virtual cornea model is constructed, the energy distribution thermodynamic diagram is generated, accurate patterning reference can be provided for irradiation treatment, and a better treatment effect is achieved.

The invention provides an osteoporosis prediction method and system based on a centrum CT image, and the method comprises the steps: obtaining a patient centrum CT image, and carrying out the preprocessing of the image, and obtaining standardized three-dimensional voxel data; performing spatial resampling on the original data to a uniform resolution; constructing a hierarchical feature extraction network for centrum bone structure perception, and designing a non-uniform sampling mechanism for centrum density distribution; constructing an intervertebral biomechanical conduction diagram network; designing an osteoporosis specific loss function; and outputting a grading prediction result containing confidence, generating probability distribution of each grade through a softmax function, and generating a visual thermodynamic diagram of the lesion area. Through time sequence consistency constraint, the system can analyze image changes of the same patient at different time points and evaluate the treatment effect and the disease progress. The dynamic monitoring ability provides a powerful tool for long-term management of chronic osteoporosis, and is helpful for timely adjustment of treatment schemes and improvement of prognosis of patients.

The invention discloses a hand function rehabilitation closed-loop training system based on a multi-dimensional evaluation model, and relates to the technical field of hand rehabilitation, a data set acquisition module collects key physiological parameters such as a joint pressure coupling coefficient, and quantifies the biomechanical relevance between joint movement and pressure distribution; the LSTM neural network synchronously generates an action completion degree score and a joint pressure coupling coefficient prediction value, and dual functions of rehabilitation quality evaluation and abnormity early warning are realized; a confidence index is generated based on a feature consistency factor of a score fluctuation standard deviation and a prediction-actual measurement deviation, and the credibility of an evaluation result is accurately identified; switching response modes in real time according to a confidence level grading interval, and dynamically adjusting training difficulty and auxiliary force parameters; a confidence quantitative model and a rehabilitation strategy engine are deeply coupled to form an'evaluation-verification-regulation 'integrated framework, so that the rehabilitation safety and effectiveness are remarkably improved.

The invention provides a physiological mechanism driving and data fusion method and system for sitting posture human body modeling, and relates to the technical field of digital twinning and biomechanics crossing. The method comprises the following steps: mapping human body macroscopic parameters into biomechanical attribute parameters; generating a basic three-dimensional human body grid based on the human body macroscopic parameters; generating a deformed three-dimensional human body grid in combination with the basic three-dimensional human body grid and the human body macroscopic parameters; solving an optimal attitude based on the vehicle geometric parameters, the task scene identifier and the biomechanical attribute parameters; finally, the deformed three-dimensional human body grid and the optimal posture are integrated, the high-fidelity sitting posture digital twinborn body is generated, end-to-end automatic modeling from macroscopic parameters to the digital twinborn body is achieved, and the core problems that the form and the posture are not matched, crowd coverage is insufficient and task adaptability is poor are solved.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention relates to a biomechanical modeling and normalized training data generation method and device for human hand posture estimation, equipment and a medium. The method comprises the following steps: acquiring target three-dimensional hand posture data of a target object; for each target object, acquiring reference static biomechanical characteristics and dynamic biomechanical characteristics corresponding to each target three-dimensional hand posture of the target object; for each target three-dimensional hand posture, determining a three-dimensional joint coordinate of the target three-dimensional hand posture and a two-dimensional joint coordinate corresponding to the three-dimensional joint coordinate based on the reference static biomechanical characteristics and the dynamic biomechanical characteristics; wherein the three-dimensional joint coordinates represent the spatial positions of the hand joint points; and determining hand posture training data based on the three-dimensional joint coordinates and the corresponding two-dimensional joint coordinates. By adopting the method, the individual biomechanical structure difference can be eliminated, and the number of training data can be expanded more accurately and reasonably.

The invention belongs to the technical field of parameterization design, and discloses an orthopedic insole parameterization design method based on biomechanical data and a rule library, which comprises the following steps: by acquiring high-precision three-dimensional scanning data of a foot below a knee joint of a child, carrying out denoising processing and three-dimensional reconstruction on a foot point cloud to obtain a three-dimensional scanning data of the foot below the knee joint of the child; geometrical characteristic parameters and mechanical distribution characteristics of the foot are identified, and regional function segmentation is carried out on the foot according to the geometrical characteristic parameters and the mechanical distribution characteristics; the foot correction demand degree is calculated by analyzing an arch flatness quantitative index and a heel posture deviation characteristic, so that a personalized design parameter range is screened out; by fusing foot geometric features, simulation analysis results and material mechanical properties, an insole design scheme is constructed and optimized, a correction effect is predicted and compared with the simulation analysis results for analysis, and a design precision evaluation result is obtained; the method is applied to a personalized insole design scheme; according to the invention, accurate identification and personalized correction of the child foot problems are realized.

The invention discloses an AI assessment method and system for the safety risk of aged furniture, and relates to the technical field of artificial intelligence assessment, and the method comprises the steps: placing a virtual body in a virtual three-dimensional environment, and positioning a mismatching risk point of the body of a user and to-be-assessed furniture through applying simulation disturbance; extracting biomechanical parameters under the risk point and determining a physical risk value; carrying out dynamic deduction through a physiological-psychological state mapping relation based on biomechanical parameters, and determining a psychological stress value; fusing the physical risk value and the psychological pressure value to generate a current safety risk assessment result; based on a virtual body, simulating an elderly weak physiological trajectory, and performing long-term safety risk prediction on furniture in the same virtual three-dimensional environment; according to the method, mechanical disturbance and typical instability working condition simulation are introduced into the virtual three-dimensional environment, so that the safety guarantee level and the prospective evaluation capability of the aged furniture under the real use condition are improved.

The invention provides a massage robot personalized physiotherapy method and system based on a cloud platform, and relates to the technical field of massage robots, and the method comprises the steps: carrying out the static posture scanning and dynamic posture capturing of a user; carrying out posture structure modeling based on a multi-modal fusion algorithm; calculating a security boundary, and marking a risk area; a massage path is planned through an improved RRT algorithm, and a personalized massage track is output; executing user massage, and monitoring massage pressure and spine displacement data in real time; abnormal values of massage pressure and spine displacement data are judged in real time, and personalized massage tracks are adjusted according to abnormal conditions. According to the method, an exclusive biomechanical digital twinborn model is established by adopting static and dynamic posture capture starting from individualization and fusing multi-dimensional data, and a safety constraint posture model is constructed through precisely quantified safety boundary calculation and risk area labeling, so that a unique massage path is planned; the individual difference requirements from the structure to the physiological tolerance are met.

The invention discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

The invention provides a rehabilitation scheme generation system and method based on a personalized state model and reinforcement learning, and aims to solve the technical problems that an existing rehabilitation scheme is static and universal and cannot be dynamically adjusted according to daily fluctuating physical conditions of a user. Comprising a multi-mode biomechanical data acquisition module used for acquiring kinematics and dynamics data of a hand of a user; the user state modeling module is used for constructing and dynamically updating a personalized state model based on the collected data; the scheme generation module is used for determining a personalized rehabilitation scheme by utilizing a reinforcement learning model based on a personalized state model and the index; and the real-time feedback module is used for comparing the real-time action of the user with a target action generated based on the personalized state model and providing deviation correction feedback. According to the method, the optimal rehabilitation strategy can be dynamically generated, accurate and safe guidance is carried out, and the personalized rehabilitation effect and safety are improved.

The invention relates to the technical field of medical hygiene, and particularly discloses a hygiene visual intelligent monitoring and guiding system suitable for surgical hand disinfection, hand motion data is collected by fusing a multi-modal motion capture module with a depth camera and an inertial sensor, and a high-risk area identification module analyzes the risk of microbial residues in finger seams and fingernail edges. The three-dimensional motion modeling module constructs a biomechanical model to calculate a trajectory, the dynamic compliance guiding module marks deviation in real time through optical projection, the multi-source data fusion module integrates a risk value and a spectral data evaluation effect, and the intelligent decision engine module outputs an operation matching degree and a correction instruction based on a neural network. Omnibearing monitoring, accurate risk assessment, real-time action guidance and quantitative effect evaluation of the hand disinfection process of an operator are realized, the standardization and effectiveness of surgical hand disinfection operation are finally improved, and the infection risk of a surgical site is reduced.

The invention provides a bone fracture rehabilitation dynamic management system and method based on multi-source data analysis. The method comprises the steps that multi-modal physiological data and biomechanical indexes of a target patient in the bone fracture rehabilitation training process are collected; constructing a multi-modal evaluation model based on a space-time convolutional neural network, and inputting the multi-modal physiological data into the multi-modal evaluation model to obtain a multi-modal rehabilitation evaluation score of the target patient; extracting a drift rate of a joint moment center and a chaos characteristic quantity of gait phase deviation in the key biomechanical indexes, performing deviation calibration on endogenous state parameters of the multi-modal evaluation model through the drift rate and the chaos characteristic quantity, and outputting rehabilitation deviation characteristics of a target patient; and generating a digital monitoring report and a personalized adjustment suggestion for bone fracture rehabilitation training of the target patient through the rehabilitation deviation characteristics and the multi-modal rehabilitation evaluation score. Based on the scheme, personalized stage adjustment of digital bone fracture rehabilitation training can be realized.

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

The invention provides an absorbable bioprosthetic valve design method based on artificial intelligence and biomechanics. The absorbable bioprosthetic valve design method comprises the following steps: S1, constructing a performance demand system of a valve; s2, constructing a three-dimensional reconstruction model of the original lesion structure of the human body and a parameterized structure model of the valve; s3, establishing a first deep learning model for simulating interventional mechanical behaviors of the valve; s4, constructing a second deep learning model for predicting the relationship between the stress and the material degradation behavior in the long-term use process of the valve; s5, embedding the first deep learning model and the second deep learning model into a reinforcement learning framework; and S6, carrying out simulation and experimental verification based on a design result in the step S5, and evaluating the adaptability and reliability of the design parameters under the function requirements defined in the step S1. According to the method, the biomechanical theory and the artificial intelligence algorithm are fused, the structural parameter interpretability of the absorbable bioprosthetic valve design and the automation of the whole process are realized, and a new path is provided for the intelligent optimization design of the implantable interventional medical device with a complex structure.

The invention discloses an integrated management system and method for postoperative care of orthopedic patients, and relates to the technical field of medical treatment. Comprising a bone mineral density dynamic monitoring value, six-dimensional mechanical parameters of a joint movement track, emotion spectrum characteristics in voice interaction, microenvironment temperature and humidity changes collected by wearable equipment and skin surface pressure distribution; inputting the recovery data into a multi-modal decision-making model based on orthopedic biomechanics improvement, and generating a nursing scheme including a skeleton stress safety interval, a virtual rehabilitation scene dynamic parameter and an interdisciplinary intervention priority; the scheme parameters are dynamically optimized through a layered reinforcement learning framework special for the orthopedics department, and the deformation logic of the 3D printing brace and the correction strategy of the nursing robot are synchronously controlled; based on the postoperative complication space-time prediction map, a multidisciplinary collaborative response instruction strictly matched with the rehabilitation stage of the patient is triggered.

The invention relates to the technical field of action recognition, in particular to a competitive sports-oriented skeleton trajectory deep learning compensation method and system, and the system comprises a data preprocessing module, a data integrity evaluation module, a core compensation network, a biomechanical rationality optimization module, a personalized habit encoder and an advanced compensation path. Compared with the prior art that a single trajectory compensation model is generally adopted, challenges of shielding scenes of different degrees are difficult to effectively deal with, and the problem of insufficient short-time shielding compensation precision or trajectory distortion under long-time shielding often occurs, an intelligent routing mechanism based on data integrity evaluation is adopted, and the method has the advantages that the complexity is reduced, and the reliability is improved. Through dynamic switching of a basic compensation path and an advanced compensation path fused with personalized prediction, adaptive processing of different shielding scenes is realized, reconstruction precision under short-time shielding is ensured, track rationality and continuity under a long-time shielding scene are remarkably improved through motion trend prediction, and the reconstruction precision is improved. And the practicability and the reliability of the system in an actual competitive environment are enhanced.

The invention belongs to the technical field of data processing, and provides a scale detection and scale conversion method and system based on an ultrasonic image, and the method comprises the steps: constructing a discrimination function fusing multi-modal features according to the spectral basis functions of different scales, and determining the scale contour of the scales in the ultrasonic image; the design can dynamically adapt to the spectral characteristics and structural characteristics of the scale, can effectively cope with the interference of scale diversity, ultrasonic speckle noise and the like of equipment of different manufacturers, and ensures the stability of scale positioning and subsequent conversion; the method comprises the following steps of: establishing a biomechanical-image deformation coupling model by taking a scale contour of a scale in an ultrasonic image as a geometric reference and combining biomechanical characteristics of an ultrasonic probe and tissues to obtain a corrected pixel distance between adjacent scale points of the scale; and curvature compensation for eliminating refraction errors of ultrasonic waves in curved tissues is introduced to correct a scale pixel-millimeter conversion coefficient, so that the pixel-millimeter conversion error rate is reduced to 1.2%, which is far lower than the level of a traditional method.

The invention relates to an oral implant positioning method and system based on deep learning. According to the method, three-dimensional point cloud data of an oral cavity is obtained and preprocessed, and a pre-trained three-dimensional deep learning segmentation model is utilized to automatically and accurately segment tooth, gingiva and alveolar bone structures. And based on the segmentation result, extracting key form and spatial relationship characteristics of the alveolar bone, and matching with an implant database to primarily select a candidate scheme. By integrating geometric constraint verification and biomechanical simulation, the candidate schemes are optimized and decided, and the optimal implant implantation position and posture meeting the safety and biomechanical requirements are calculated. And finally, automatically generating digital operation guide plate data completely matched with the gingival form of the patient according to the optimal scheme. According to the method, automation and intellectualization of the whole process from data collection to surgical guide plate generation are achieved, the defects that a traditional method depends on artificial experience, is high in subjectivity and low in efficiency are effectively overcome, and the precision, efficiency and reliability of implantation surgical planning are remarkably improved.

Aspects of the subject disclosure may include, for example, obtaining current video content of a player repeatedly performing a physical skill, analyzing the current video content based on previous video content, the previous video content comprises other video content of the player repeatedly performing the physical skill, determining biomechanic metrics of the player performing the physical skill based on the analysis, and determining each biomechanic metric of a portion of the biomechanic metrics does not satisfy a respective biomechanic metric success rate. Further embodiments include generating a first image of the player performing the physical skill from the current video content, generating a second image of the player performing the physical skill from the previous video content, and presenting the first image and the second image simultaneously and indicating the portion of the biomechanics that did not satisfy the respective biomechanic metric success rate. Other embodiments are disclosed.

The invention relates to an early screening method and system for abnormal spinal curvature based on multi-modal data fusion, and the method comprises the steps: carrying out the fusion of space-time sequence features based on obtained multi-modal data, and carrying out the three-dimensional biomechanical modeling, and obtaining a spine-pelvis-lower limb dynamic coupling network; carrying out weighted calculation on the key mechanical transmission path through a graph attention mechanism based on a dynamic coupling network to obtain a path strengthening feature vector; performing multi-modal risk quantitative modeling based on the path strengthening feature vector to obtain a multi-dimensional risk index; and based on multi-dimensional risk indexes, carrying out grading early warning decision making through threshold value comparison and dynamic risk layering so as to realize early screening of spinal curvature abnormity. By implementing the scheme, the early screening accuracy of the abnormal spinal curvature can be improved.

Orthopedic implants and related surgical methods for using same. The implants have suture bore geometries that facilitate performance of the surgical methods, thereby providing for improved optimal biomechanical force application in various anatomies. The implants include suture bores that have an angled / diagonal, or skewed, orientation within the anatomical planes (lateral / sagittal and frontal / coronal). The suture bores have the skewed orientation so that the adjacent soft tissues (i.e., tendons or ligaments) can be advanced via the suture therethrough in superior-inferior and inferior-superior directions. Openings, or holes, at the ends of the suture bores are configured to approximate the adjacent associated soft tissue to the implant.

The invention relates to the technical field of dental implants in oral medicine, in particular to an optimal design of a geometric structure of a dental implant. According to the technical scheme, the method comprises the following steps: firstly, specifying cross section general forms of three types of implants, then, creating a natural tooth model based on multiple groups of head image data, counting the value range of the curvature radius of each type of natural teeth based on the natural tooth model, and obtaining the feature size of the implant and the corresponding optimal curvature radius thereof; according to the dental implant simulating the natural tooth root curvature and the design and implantation system thereof, the morphological characteristics of natural teeth are fully considered, the specific shape of the cross section of the upper end face of each type of implant under each feature size is determined, and finally the dental implant model is created based on the results. The oral cavity biomechanical requirement and the functional adaptability of the human body oral cavity can be better met, meanwhile, large-scale production can be achieved, and the requirement of large-scale clinical application can be met.

The invention provides a heavy competition action recognition method based on kinematics constraints, and aims to solve the problems that the existing action recognition technology is insufficient in capturing high-speed motion joint features and lacks physiological rationality constraints, and high-precision action classification is realized by fusing a kinematics correction mechanism and a layered feature isolation architecture. Specifically, the joint kinematics correction layer is designed, illegal joint angles are automatically corrected according to biomechanical constraints of the re-competitive actions, and a skeleton sequence conforming to ergonomics is generated; meanwhile, a body and limb double-branch isolation encoder is constructed, core trunk and tail end limb features are processed respectively, and multi-scale spatial-temporal features are aggregated through a cross-level fine-grained fusion module; in addition, a dynamic calculation path selection mechanism is introduced, and the network depth is adaptively adjusted according to the complexity of the action. The method effectively solves the problems of high-speed action detail loss and non-physiological attitude interference.

The invention belongs to the technical field of medical treatment, and discloses a surgical robot arm joint torque collaborative optimization and trajectory tracking control method, which comprises the following steps of: dynamically associating a tissue viscoelasticity characteristic with a joint torque through biomechanics-multi-body dynamics coupling modeling to form a'mechanics-injury 'double-constraint mechanism; a damage early warning factor is introduced into the memory enhanced controller, torque output is adjusted in real time when an instrument interacts with a tissue, and traction damage caused by overlarge load of a single joint is avoided; meanwhile, the digital twin system prejudges potential risks in advance, the threat of surgical operation to fragile tissues is reduced from modeling, control to verification of a whole link, and a safety guarantee is provided for operation in a fine surgical area; through staged dynamic weight optimization, target adaptive adjustment of different operation stages is realized; the multi-modal sensing fusion architecture combines flexible sensing and image data, and improves the sensing precision of a complex organization environment.

The invention relates to the technical field of pressure monitoring, and discloses a method and system for motion state analysis according to plantar pressure, and the method comprises the steps: receiving multi-modal biomechanical data related to a landing event; extracting a corresponding motion feature vector from the multi-modal biomechanical data; and inputting the motion feature vector into a motion state analysis model for identification to obtain a corresponding damage risk index, and outputting the corresponding damage risk index. By receiving pressure distribution data detected by the plantar pressure sensing array, shear force data detected by the shear force measuring and calculating module and a kinematics data sequence detected by the inertial measurement unit, biomechanical information in the movement process can be comprehensively captured from multiple dimensions; compared with a single data acquisition mode, the fusion of the multi-modal data can reflect the real state of the human body in motion more accurately, and a richer and more accurate data basis is provided for subsequent motion state analysis.

The invention discloses a control method, and the method comprises the steps: collecting the data of a centroid state and an MPC optimal ground acting force in a plurality of motion states of a robot, designing a full-connection residual network, taking the centroid state information as the input, taking the ground acting force as the output, and designing a loss function based on priority. Upper and lower bounds of a friction cone and a sole reaction force are constrained, and the network is trained offline to fit MPC optimization quantity, so that an efficient mode of online reasoning is realized; the joint angle and the angular velocity are fused with the action at the previous moment, the centroid linear velocity and the angular velocity and the foot-ground acting force fitted through a residual network, a bionic reference trajectory is generated through a human motion public data set, different weights are given to a reward function group according to a dominant function value obtained through calculation, and the control performance is improved; symmetrical loss constraint is added to lower limb movement, and the algorithm convergence speed is increased. And finally, training to obtain a whole-body control strategy based on model priori knowledge and a human motion reference trajectory.

Exemplary task-agnostic exoskeleton control system and method are disclosed that are task-agnostic utilizing instantaneous estimates of biological joint moments from deep neural networks to assist the user movements. The exemplary control system employs multiple body sections and joints in-the-loop estimation to provide multi-joint assistance operation, e.g., within an autonomous, clothing-integrated exoskeleton. The exemplary control system may deploy a deep domain adaptation (DDA) method configured to translate human movement data between a simulated sensor domain and a real sensor domain.