935 results about "Biomechanics" patented technology

The invention relates to the technical field of medical rehabilitation, in particular to an artificial intelligence-based orthopedic full-cycle rehabilitation management system, which comprises a rehabilitation demand evaluation module, a three-dimensional pathological modeling module, a multi-modal training generation module, a rehabilitation effect prediction module, a dynamic parameter adjustment module and a full-cycle management module. Multi-dimensional data such as bone mineral density, joint motion range, gait mechanics and psychological state of a patient are collected, an artificial intelligence algorithm is combined for analysis, a personalized rehabilitation scheme is generated, and rehabilitation parameters are predicted and adjusted in real time. The system can dynamically correct the training intensity, the nutritional supplement dosage, the psychological intervention mode and the rehabilitation schedule, and the safety and reliability of data are guaranteed through the block chain technology; according to the system, biomechanics, nutriology, psychology and environmental factors are comprehensively fused, the system has strong adaptive ability and full-period management ability, real-time optimization can be performed according to the rehabilitation process of a patient, and the rehabilitation effect of the orthopedics department and the satisfaction degree of the patient are remarkably improved.

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

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 plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The invention provides an artificial intelligence auxiliary diagnosis method and system for a knee joint, and the method comprises the steps: synchronously collecting a multi-angle dynamic X-ray image and six-degree-of-freedom contact force distribution data in an articular cavity, dynamically tracking a patella sliding track, and generating a mechanical phase track; a personalized knee joint model is constructed in combination with the biological parameters of the lower limbs of the patient, the mechanical phase trajectory and the contact force data vector are superposed to generate a dynamic mechanical fingerprint, and then the dynamic mechanical fingerprint is mapped into a biomechanical attribute map; electromyographic signal time-frequency characteristics and tibia rotation angles in the movement process are collected, an electromyographic activation mode is constructed, and the electromyographic activation mode and a biomechanical attribute map are subjected to characteristic fusion to form a joint dynamics comprehensive contour; analyzing a stress wave conduction path by using a joint anomaly dynamic propagation model to identify a compensatory injury mode; a diagnosis map is generated according to the recognition result, a preset biomechanical constraint condition is matched to correct the diagnosis map, and an individualized rehabilitation strategy is formulated. The treatment effect is improved, and the recovery speed of a patient is increased.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

The invention provides a personalized fitness system and method based on multi-modal data and an adaptive large model. According to the method, multi-modal data are integrated, and heterogeneous data collaborative analysis is realized by using a space-time alignment algorithm and a confidence coefficient weighting mechanism. A multi-task learning framework is adopted, physiological prediction, exercise risk early warning and psychological incentive strategy generation tasks are synchronously processed, a user stage target is adapted through a dynamic attention mechanism, and federal learning and transfer learning technologies are combined. A nonlinear periodic planning engine based on reinforcement learning dynamically adjusts training load and action difficulty according to real-time biomechanical simulation results and recovery state evaluation, and meanwhile, a lightweight model compression technology and end-side reverse dynamics calculation are adopted. Based on the scheme, the exercise performance prediction precision and the early warning timeliness are improved, and the exercise loss risk caused by overtraining is reduced; the cold start data bottleneck is broken through; low-delay virtual coach interaction and privacy protection are realized; and dynamically adjusting the training intensity and the incentive strategy.

The invention belongs to the technical field of medical robot control, and discloses a physiotherapy robot self-adaptive control method and system based on multi-mode biological feedback, and the method comprises the steps: carrying out the preprocessing of a biomechanical sensing signal obtained in advance, carrying out feature extraction and dynamic weight fusion on the preprocessed biomechanical sensing signal by utilizing wavelet transform and frequency domain transform to obtain a joint feature; establishing a system dynamics model based on a Lagrange equation, generating a time-varying impedance trajectory model by utilizing a joint state acquired in real time and combining a rolling optimization algorithm, and dynamically adjusting impedance parameters of the time-varying impedance trajectory model according to joint features to generate a safe motion trajectory; and performing double closed-loop control on the physiotherapy robot according to the safe movement track and a biomechanical sensing signal obtained in real time to obtain a control signal of the physiotherapy robot, and realizing joint motor driving of the physiotherapy robot based on the control signal. The device can be flexibly changed according to individual differences of patients and rehabilitation processes.

The invention discloses a spine three-dimensional evaluation method and system based on RGB images and anatomical constraints, and belongs to the field of medical image analysis. The method comprises the following steps: carrying out preprocessing and anatomical symmetry correction on an upright position back RGB image shot by the intelligent mobile equipment for a single time; extracting regions of interest of shoulder blade and iliac crest mark points by using a lightweight segmentation network; generating an initial depth map through a depth estimation model fusing a vertebra attention mechanism and a multi-constraint loss function; carrying out physiological scale calibration on the initial depth map based on height and weight parameters; a cascade GAN is adopted to eliminate clothes interference and reconstruct a back three-dimensional surface; based on biomechanical characteristics, NURBS parameters are adjusted in a self-adaptive mode to fit a spine three-dimensional curve; and outputting a coronal plane Cobb angle, a trunk rotation angle and a sagittal plane curvature parameter. Through monocular depth estimation and dynamic evaluation of anatomical constraint, the problems of radiation risk, subjective error and clothes interference of a traditional method are solved.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

The invention provides a muscle stimulation adjusting method and system based on gait detection. The method comprises the following steps: acquiring lower limb biomechanical data; performing phase recognition according to a dynamic programming algorithm, and constructing a pressure center track to perform feature extraction to obtain gait features; carrying out feature analysis on the gait features through a reverse dynamics model and a machine learning model, and carrying out output judgment according to a double-model collaborative decision-making mechanism to obtain a stimulation parameter decision-making result; generating an electrical stimulation waveform according to the decision result, and applying differential electrical stimulation to the target muscle in the gait cycle to obtain a stimulation scheme; and monitoring stimulated myoelectricity feedback signals and gait changes, and carrying out parameter updating by calculating a signal change rate and analyzing a motion track error to obtain an updating scheme. According to the method, through a double decision-making mechanism of the reverse dynamics model and the machine learning model and a real-time feedback regulation mechanism, the gait phase recognition precision is improved, and precise regulation and control of stimulation parameters are realized.

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 discloses a scoliosis orthosis registration method based on deep learning and biomechanical constraint, and relates to the technical field of scoliosis orthosis design, and the method comprises the steps: respectively obtaining first feature information of a three-dimensional spine model and second feature information of a three-dimensional body surface model, the second feature information comprises a geometric feature, a pressure feature and a motion feature; the first feature information and the second feature information are simultaneously input to a pre-constructed hybrid configuration network for rigid registration and non-rigid registration, the rigid registration outputs an initial alignment model, and the non-rigid registration outputs high-precision local adaptation data with reasonable dissection; and performing joint optimization on the initial alignment model and the high-precision local adaptation data with reasonable dissection to obtain a final registration result. Geometric and motion features of a spine and a body surface are extracted through double-flow features, so that a registration network of coarse rigidity and then fine non-rigidity is carried out, and transition from geometric registration to function optimization is realized.

The invention relates to the technical field of medical rehabilitation training, in particular to an intelligent hand training method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a training mode instruction selected by a user; real-time biomechanical data of the affected side hand are collected through a multi-source sensor array, the real-time biomechanical data comprise finger joint angles, electromyographic signal intensity and a touch pressure distribution matrix, and when the affected side hand is in a mirror image training mode, the six-degree-of-freedom motion trail of the uninjured side hand is synchronously collected; calling a corresponding algorithm model for processing based on the training mode, and generating a control instruction set containing pneumatic pressure parameters, electrical stimulation waveforms and virtual interaction instructions; the control instruction set is sent to a server side, a mechanical glove arranged on the hand of the affected side in a sleeving mode executes corresponding pneumatic-electric combined driving operation, and multi-sense feedback is synchronously presented. According to the application, the composite requirements of patients in different rehabilitation stages on training mode diversity, control accuracy and interactive immersion can be met.

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 skeleton model manufacturing method based on a 3D printing technology, and relates to the technical field of medical treatment, and the method comprises the steps: collecting the multi-modal image data of the skeleton of a patient, carrying out the image segmentation and reconstruction, and generating a digital three-dimensional model of the skeleton of the patient; carrying out mechanical optimization and material distribution adjustment on the digital three-dimensional model by utilizing a topological optimization algorithm, designing a bionic microstructure, and outputting a three-dimensional skeleton model subjected to optimization and bionic design; the bone model which is detected to be qualified is used for generating a personalized mold, and a prosthesis which is completely matched with the bone of the patient is customized through the mold; preoperative simulation and design of a prosthesis implantation scheme are carried out based on the skeleton model, and a personalized prosthesis and an operation scheme matched with the skeleton of the patient are obtained; the structural rigidity is maximized and the material consumption is minimized by utilizing a topological optimization algorithm, so that the bone model can better meet the biomechanical requirements of a patient, and meanwhile, the bionic characteristics and mechanical properties of the model are enhanced through the bionic microstructure design.

The invention discloses an automatic braised chicken packaging and detecting method based on machine vision, and belongs to the field of food processing automation and quality detection. The method comprises the steps of obtaining packaging environment parameters to generate compensation parameters; adjusting the imaging unit based on the compensation parameter to generate optimized imaging data; performing multispectral feature extraction on the optimized imaging data to generate a defect map; analyzing product forms and foreign matters based on the atlas and a preset database, and generating a judgment result and a confirmation mark; and fusing the judgment result and the confirmation mark to generate a comprehensive diagnosis instruction. According to the method, technologies such as environmental parameter compensation imaging, multispectral characteristic analysis, biomechanical form matching and thermophysical foreign matter confirmation are adopted, the quality problems in multiple aspects such as package sealing, content form and tiny foreign matter can be comprehensively detected, and the accuracy, reliability and anti-jamming capability of automatic package detection in a complex production environment are remarkably improved.

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 discloses an edentulous jaw dental implant positioning method combined with depth variation reasoning. The method comprises the following steps: S1, generating optimized medical image data; s2, generating a jaw bone three-dimensional reconstruction model of the edentulous jaw patient; s3, generating anatomical feature data; s4, generating a preliminary implant positioning scheme; s5, generating a local bone reconstruction simulation result; s6, performing dynamic interaction on a local bone reconstruction simulation result and the preliminary implant positioning scheme, optimizing the preliminary implant positioning scheme through a depth variation inference model, and generating an implant optimized positioning scheme including mechanical stability and biomechanical performance evaluation; and S7, based on the implant optimization positioning scheme, generating implant guide plate design data and surgical navigation data in combination with the jaw bone three-dimensional reconstruction model of the patient. According to the method, the final implant positioning point obtains a globally optimal solution in the aspects of long-term stability and biological adaptability.

The invention provides a personalized training plan generation method and system in meta-universe smart sports, and relates to the technical field of smart sports, and the method comprises the steps: obtaining user meta-universe scene sports data, calculating an actual sports load based on a human body biomechanical model, carrying out the time sequence analysis through a neural network, and predicting a physiological limit threshold value; and constructing a training knowledge graph, generating a stage training plan adaptive to the user through a graph neural network, and finally presenting the stage training plan in a meta-universe virtual scene form. According to the method, personalized, safe and scientific training guidance can be provided, and the training effect and the user experience are 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 belongs to the technical field of application of an artificial intelligence technology to medical rehabilitation instruments, and relates to a method for designing an orthosis based on an AI artificial intelligence technology, and the method comprises the steps: S1, data collection and preprocessing: converting patient data into structured data; s2, feature extraction: extracting geometric and biomechanical features related to orthosis design, and constructing a feature set; s3, processing the features by using a deep learning model, and predicting personalized parameters; s4, designing and optimizing an orthopedic module, and optimizing pressure distribution; s5, a three-dimensional orthopedic CAD system is developed and deployed, and patient information and orthopedic device design are managed; and S6, evaluation and optimization: fusing clinical data, and optimizing the system according to an evaluation result. According to the method, the AI technology is utilized to improve the design efficiency and the adaptation accuracy, the personalized requirements are met, the rehabilitation effect is optimized, digital management of data and iteration of an AI model are achieved, a treatment basis is provided for rehabilitation medical employees such as a brace teacher and a rehabilitation therapist, and the clinical effectiveness of a rehabilitation treatment scheme is improved.

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 relates to the technical field of pelvic floor muscle training, in particular to a pelvic floor muscle recovery training system. The system comprises a physiological data acquisition module, a feature extraction and analysis module, a model training and optimization module, a personalized training plan module, a dynamic training adjustment module, a three-dimensional mapping and modeling module, a biomechanical simulation module and an intelligent state recognition module. According to the invention, through fusion of a convolutional neural network and a time sequence analysis method, key physiological features are extracted and analyzed, a personalized training plan module is combined with an intelligent algorithm, a training scheme is customized for a user, specific requirements are matched, and a dynamic training adjustment module monitors and adjusts training parameters in real time. The three-dimensional mapping and modeling module provides a muscle model by using an imaging technology and a three-dimensional reconstruction algorithm, the biomechanical simulation module simulates the mechanical response of muscles through a finite element analysis and computer simulation technology, and the intelligent state recognition module analyzes the muscle state by using a support vector machine technology.

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.