49 results about "Biomechanical model" patented technology

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

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

The present application relates to the technical field of gait analysis, and discloses a gait plantar pressure intelligent prediction system based on a Transformer structure, which comprises a gait collection platform, a plantar pressure collection device, a gait kinematics collection device, a central processing server and a display terminal. The system obtains initial plantar pressure distribution through a lower limb multi-rigid-body chain biomechanical model and foot-ground contact mechanics solution; and constructs a gated prefix memory sliding window Transformer in the central processing server, performs time series modeling and spatial correction on the initial pressure matrix and gait characteristics, and generates plantar pressure prediction results conforming to the biomechanical law. The system effectively solves the problems of unstable prediction, weak generalization ability and lack of interpretability of traditional methods, realizes higher-precision plantar pressure prediction, and can be used for gait evaluation, rehabilitation training and orthosis customization.

The application discloses a kind of Chinese fifth percentile old male passenger injury bionical model with sign, also called biomechanical model, is the digital computing tool in the technical field of intelligent high-end detection equipment of automobile safety.This application is based on the fifth percentile male automobile passenger injury bionical model of thurstone, its each part is adjusted with age-related change to represent old male.The injury bionical model provided by the application meets the Chinese fifth percentile old male sign parameter;The model has detailed human anatomy structure characteristics.The Chinese fifth percentile old male passenger injury bionical model developed and constructed in the application not only provides a high-fidelity simulation tool for studying the injury mechanism of Chinese old passengers during the collision process, but also helps to deepen the understanding of the complex relationship between age change, body geometry and tissue mechanical properties and injury risk.

The invention discloses a wearable robot assistance parameter adjusting method and system based on a biomechanical model, relates to the field of wearable robots, and solves the problem that a current wearable robot cannot intelligently adjust an assistance torque and a damping coefficient. The method comprises the following steps: constructing a test single-step period standard deviation and a standard plantar pressure interval of legs of a wearer; according to the difference value, selecting an initial assistance torque and an initial damping coefficient when the current wearer uses the wearable robot; analyzing plantar pressure at different time nodes after the wearer uses the wearable robot to assist; whether the wearable robot needs to be adjusted or not is analyzed according to the actual plantar pressure and the actual single-step cycle standard deviation; according to the plantar pressure deviation rate and the single-step period deviation rate, the initial power assisting torque and the initial damping coefficient of the wearable robot are adjusted. According to the method, adaptive adjustment of the power assisting parameters of the wearable robot is achieved according to the plantar pressure deviation data and the single-step period deviation data.

The present invention relates to a device (1) and method for generating a geometrical model (33) and a 3D biomechanical model (31) of a spine of a patient and assisting a user in planning a spine treatment for said patient's spine. The present invention further relates to a surrogate model. The 3D biomechanical model (31) and the surrogate model may be configured to generate biomechanical performance indicators and clinical indicators.

The application relates to a method for estimating the biomechanical parameters of in-vivo organ tissues, which comprises the following steps: in-vivo organ tissue geometry modeling is performed to obtain a tetrahedral mesh structure of the in-vivo organ tissues; according to the obtained tetrahedral mesh structure of the in-vivo organ tissues, in-vivo organ tissue biomechanical model modeling is performed; according to the in-vivo organ tissue biomechanical model, in-vivo organ tissue boundary condition estimation is performed; and the strong tracking Kalman filter is used to estimate the biomechanical parameters of the in-vivo organ tissues under the current boundary condition estimation result. The application can estimate the biomechanical parameters of in-vivo organ tissues of human bodies under complex boundary conditions, and the estimated biomechanical parameters are more accurate.

The invention provides a force feedback instruction output method in neurosurgical operation simulation, which comprises the following steps: S1, initializing force feedback equipment, creating a high-frequency service circulation thread and respectively configuring independent control channels of left and right hand surgical instruments; s2, collecting encoder data, joint angles and surgical instrument tail end pose information of the force feedback equipment in real time; s3, analyzing the data flow of the encoder data, and extracting a clamping angle state signal of the surgical instrument, a suction force state signal of an aspirator and an instrument replacement control signal; s4, dynamically calculating an interaction force between the nervous tissue and the surgical instrument based on the biomechanical model, and generating a six-degree-of-freedom force feedback instruction; s5, sending the feedback instruction to force feedback equipment, and updating the state of the instrument model in the three-dimensional operation scene; and S6, monitoring the running state of the force feedback equipment, and closing feedback output and releasing resources when the force feedback equipment is abnormal or the operation is terminated. The tactile feedback core pain point in neurosurgery simulation is solved.

The application discloses a heart soft tissue model correction method and device based on a physical information neural network, and comprises the following steps: acquiring preoperative images to construct a personalized heart biomechanical model and an active motion priori library; constructing a double-flow coupled physical information neural network, which comprises an active deformation prediction branch for predicting heart autonomous beats and a passive residual correction branch for correcting deformations caused by external contact forces; defining a loss function comprising a data matching loss and a biomechanical control equation loss, and training the network; receiving in-surgery collected cardiac phase data and sparse surface displacement data, inputting the data into the trained network, and outputting a global three-dimensional deformation field to update a preoperative three-dimensional model. The application effectively decouples the active and passive mixed deformation of the heart through a double-flow coupled architecture, and can infer the full-field volume deformation by only using sparse surface observation points by embedding physical control equations as strong constraints, thereby overcoming the data sparsity problem.

A system and method for a transportation device that utilizes an alternative biomechanics framework for the human lower body that may be used in any transportation vehicle in conjunction or as an alternative to wheels whereby the bones, muscles, and connective tissue of the lower body function together to produce the mechanics of a single wheel rather than the conjoined pendulums of the leading inverted pendulum model of biomechanics.

The invention discloses a personalized spine orthopedic pillow based on ergonomics and a using method thereof, and belongs to the technical field of medical auxiliary instruments. Based on the ergonomic design, the adaptation degree of the orthopedic pillow with the head and the neck and chest of the patient is improved; through a neck-chest combined biomechanical overall model, collaborative correction of cervical retrobow and scoliosis is achieved, and recovery of overall mechanical balance of the spine is promoted. The pressure value of the air bag in the orthopedic pillow can be quantitatively set according to the individual characteristics of the patient, the personalized correction requirements of different patients are met, and the comfort of the patient is improved. Through the combination of a cervical vertebra-thoracic vertebra biomechanical model, acting forces including supporting force acting on the cervical vertebra, coronal plane pressure acting on the thoracic vertebra scoliosis area and horizontal plane rotation removing force are applied to the cervical vertebra reverse arching area and the thoracic vertebra scoliosis area of a patient. Cooperative correction of cervical vertebra reverse arching and thoracic vertebra lateral bending is achieved, so that recovery of overall mechanical balance of the spine is facilitated, and the stability and the curative effect of spine malformation correction are improved.

The application discloses a whole-body-local multi-scale biomechanical model coupling construction method for human body impact simulation, comprising the following steps: constructing a whole-body dynamics model, obtaining the kinematic response and dynamic response of the human body under a preset impact working condition; determining the connection constraint position corresponding to a target local area in the whole-body dynamics model, and extracting the constraint force and / or constraint moment at the connection constraint position; performing equivalent conversion on the constraint force and / or constraint moment to obtain equivalent load for representing the whole-body dynamics effect acting on the target local area; constructing a local simulation model of the target local area, and applying the equivalent load to the local simulation model to realize simulation analysis of the human body impact response. Through the conversion of the whole-body dynamics constraint force to the local simulation model, the application reduces the calculation complexity of the human body impact simulation while retaining the whole-body inertia effect, and improves the simulation efficiency and calculation stability.

A computer implemented method includes retrieving data from a patient's updated electronic medical record relevant to a diagnosis of a disease. The data includes medical images and at least one of the patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, and medical insurance information. The method includes generating available treatment path options including at least one surgical treatment or intervention, using at least one model to predict at least one implication for each of the available treatment path options based on the data and simulated adverse outcomes of the at least one surgical treatment or intervention simulated by predictive models including a biomechanical model based on the medical images. The method includes interactively updating and displaying a decision tree including the available treatment path options each with a corresponding at least one objective function.

This invention discloses a method and system for adjusting assist parameters of wearable robots based on a biomechanical model, relating to the field of wearable robots. It addresses the current problem that wearable robots cannot intelligently adjust assist torque and damping coefficient. The method involves: constructing the standard deviation of the single-step cycle and the standard plantar pressure range for the wearer's legs; selecting the initial assist torque and initial damping coefficient when the wearer uses the wearable robot based on the difference values; analyzing the plantar pressure at different time points after the wearer uses the robot; analyzing whether the wearable robot needs adjustment based on the actual plantar pressure and the actual single-step cycle standard deviation; and adjusting the initial assist torque and initial damping coefficient of the wearable robot based on the plantar pressure deviation rate and the single-step cycle deviation rate. This invention achieves adaptive adjustment of the wearable robot's assist parameters based on plantar pressure deviation data and single-step cycle deviation data.

The present application relates to the technical field of healthcare informatics, and specifically provides a joint torque prediction method, medium and equipment fused with biomechanical information; the method is to predict joint torque by using a joint torque prediction neural network model embedded with biomechanical information; a personalized biomechanical model is embedded in the joint torque prediction neural network model; when training the joint torque prediction neural network model, the joint torque prediction neural network model and the personalized biomechanical model are cooperatively driven and parallelly optimized based on data errors and physical residual errors, and bidirectional back transmission of gradients between the joint torque prediction neural network model and the personalized biomechanical model is realized. The intelligent estimation framework fused with biomechanical prior knowledge realizes efficient and physiologically reasonable torque prediction.

The invention discloses a method and a system for determining the elastic modulus of an internal fixation block for fracture, and relates to the field of medical instruments, and the method comprises the following steps: building a parameterized biomechanical model, extracting features from the parameterized biomechanical model, and selecting the elastic modulus of the internal fixation block based on a gradient boosting decision tree model. An artificial intelligence algorithm combined with pathological state correction is used for accurately determining the elastic modulus required by the fracture internal fixation block, and the elastic modulus is creatively fused into the physiological state index of a patient to dynamically adjust the weight coefficient. Objective customization of personalized elastic modulus is achieved, it is ensured that the internal fixing block is highly matched with the bone mechanical property of a patient, stress conduction and distribution of a bone fixing interface are effectively optimized, the stress shielding risk is reduced to the maximum extent, fracture healing quality and speed are remarkably improved, complications are reduced, and fixing stability and long-term curative effect are improved. The system is a key breakthrough for intelligent and precise orthopaedic treatment.

The invention relates to the technical field of medical image analysis and surgical planning, and discloses a mammary gland structure visual surgical planning method based on three-dimensional reconstruction, comprising the following steps: S1, acquiring diffusion tensor imaging data and biomechanical attribute data of mammary tissue; s2, based on diffusion tensor imaging data; s3, acquiring biomechanical attribute data; s4, embedding the catheter network topological structure into the mammary gland three-dimensional model; s5, simulating surgical operation and calculating stress distribution, caused by the surgical operation, on the catheter network topological structure; and S6, based on stress distribution. Multi-modal image data including diffusion tensor imaging data and biomechanical attribute data are fused and applied to breast surgery planning, and a biomechanical model with deep coupling of a microscopic catheter network topological structure and macroscopic tissue mechanical characteristics is established. The problem that structural details and mechanical response are difficult to comprehensively consider by an existing planning method is solved.

A system for surgical planning and assessment of spinal pathology or spinal deformity correction in a subject, the system comprises a control unit configured to align one or more vertebral bodies of a biomechanical model to one or more vertebral bodies of the radiograph. The control unit is configured to receive one or more spinal correction inputs. The control unit is configured to, based on the received one or more spinal correction inputs, simulate the biomechanical model in a predetermined posture. The control unit is configured to provide for display one or more characteristics of the simulated biomechanical model.

The present application relates to the technical field of intelligent wedge-shaped cushion curvature regulation, in particular to an intelligent wedge-shaped cushion curvature dynamic regulation system based on user spine physiological data, wherein a physiological data calculation module is connected with a flexible film pressure sensor array, receives and collects pressure distribution data, and calculates user spine tension physiological parameters in real time based on a preset biomechanical model, wherein the spine tension physiological parameters at least include lumbar stress load. The present application arranges a flexible film pressure sensor array on the wedge-shaped cushion body, collects pressure distribution data of the user's hips and legs in real time, and combines with the preset biomechanical load model to accurately calculate the spine tension physiological parameters including the lumbar stress load; the subsequent dynamic feedback control module can generate regulation instructions to drive the angle adjusting mechanism in real time according to the deviation of the parameters from the preset comfortable threshold range, form a closed-loop feedback control, and return to the comfortable interval until the physiological parameters.

The application discloses a kind of based on binocular camera's graph convolution enhancement abdominal organ real-time tracking method, comprising the following steps: collecting data and being marked, preoperative model is constructed and is trained, reconstructs three-dimensional model and is optimized, adopts co-rotating finite element modeling, constructs biomechanical model, carries out preoperative initial registration, eliminates the change between preoperative and intraoperative imaging of patient, extracts a variety of abdominal organ features and matches feature points, carries out intraoperative deformation registration and dynamically tracks feature points, and verifies experimental results;The application efficiently solves the problem of complex deformation of soft tissue, accurately selects key feature points through deep learning algorithm, and creates displacement vector field, provides boundary conditions for biomechanical model in time, can effectively simulate various complex deformations, and can adaptively track the dynamic changes of soft tissue during operation, significantly improve the capture and modeling ability of complex deformation behavior of soft tissue.

The application discloses a kind of intelligent prosthesis control method and system with feedback mechanism, and the application relates to intelligent prosthesis control technical field, comprising the following steps: S1: acquisition step: through the sensor cluster of prosthesis foot bottom, ankle joint and knee joint deployment, real-time acquisition terrain feature, joint motion parameter and residual limb electromyogram signal.The intelligent prosthesis control method and system with feedback mechanism, through the double driving architecture of biomechanics cognitive model and reinforcement learning strategy model, in combination with the double verification mechanism of mutual inspection arbitration unit, realizes the adaptive accurate matching of feedback signal type, intensity and timing under complex dynamic environment.Biomechanics model ensures that feedback parameters meet clinical safety boundary, reinforcement learning model optimizes environmental adaptability in real time, mutual inspection arbitration resolves model conflict through physiological tolerance screening and control stability prediction, solves the problems of control misalignment, too high user cognitive load and walking confidence decline caused by traditional static rule feedback.

The present application is suitable for the field of 3D scanning technology, and provides a character model 3D scanning and dynamic posture capturing method and system. The method dynamically triggers three-dimensional scanning by real-time monitoring of joint movement intensity, continuously collects continuous motion trajectories using inertial sensors during non-scanning periods, and inversely calculates the space-time parameters of the occluded part through multi-light source shadow boundary evolution. A space-time joint evaluation function is constructed to adaptively adjust the scanning frequency and sampling rate. Finally, the discrete point cloud, continuous trajectory and inverse calculation data are space-time aligned and confidence weighted fused to generate a dynamic biomechanical model. This scheme breaks through the resource waste and occlusion blind zone bottleneck of traditional fixed frequency scanning, realizes the space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as film and television animation, medical rehabilitation, sports training, etc. Through multi-modal data fusion and adaptive resource scheduling, the system significantly improves the data integrity, real-time performance and biomechanical analysis capability, providing an efficient solution for human motion digitization.

The application discloses a blood pressure calculation method and equipment based on cross-scale fluid-solid coupling and considering nonlinear biomechanical characteristics of an aorta, and the method comprises the following steps: establishing a three-dimensional ideal aorta geometric model; establishing a zero-dimensional heart lumped parameter model to simulate the process that the heart pumps blood to the aorta inlet through periodic contraction and diastole; establishing a zero-dimensional three-element Windkessel model of small arteries to simulate blood flow microcirculation of peripheral small arteries; establishing a blood vessel wall biomechanical model containing residual stress of the aorta wall and active contraction of smooth muscle cells to obtain a pressure-radius relationship of the aorta wall as a fluid wall boundary condition to simulate the change process of the aorta radius in blood circulation; and coupling the three-dimensional ideal geometric model of the aorta, the zero-dimensional heart lumped parameter model and the zero-dimensional three-element Windkessel model to construct a cross-scale fluid-solid coupling system of hemodynamics, and the blood pressure in the aorta is calculated through iterative solution.

The application discloses a mapping method for evaluating human body impact damage degree based on animal injury condition equivalence, and belongs to the technical field of impact damage biomechanics, and comprises the following steps: constructing a whole-body high-fidelity biomechanical model of different species, applying underwater explosion vibration impact loads with different intensities to the model to obtain damage modes; statistically classifying the damage modes, integrating the damage modes to form a damage database, quantitatively calculating the damage in the damage database by using a new damage severity scoring system, selecting multiple physical factors affecting the biological damage degree, screening out two key physical quantities with the highest contribution rate to define scaling coefficients, and drawing a mapping atlas for evaluating the human body impact damage degree based on animal injury condition equivalence; the mapping atlas of the injury condition across species is established by constructing the multi-species biomechanical model and combining principal component analysis, and the accurate evaluation from animal experimental data to the human body damage degree under the underwater explosion vibration impact environment is systematically realized.

This invention relates to the field of edge computing technology for medical monitoring, specifically to an orthopedic rehabilitation monitoring network system and method based on edge intelligent decision-making technology. The method discloses an orthopedic rehabilitation monitoring network method based on edge intelligent decision-making technology. This method acquires standardized multimodal sensory data from patients, and a local edge computing node calls a built-in lightweight skeletal muscle biomechanical model for real-time calculation, extracting joint range of motion, electromyographic envelope, and gait spatiotemporal parameters. These parameters are then input locally into a pre-trained posture anomaly detection decision tree for frame-by-frame comparison and analysis. When any parameter deviates from a preset standard, a posture anomaly event label and associated sensor data slice are immediately generated locally. This lightweight alarm information is transmitted to a regional rehabilitation monitoring center server, triggering the retrieval of high-definition images. This method achieves local real-time intelligent detection and early warning of rehabilitation anomalies, reducing network load and response latency.

A method for predicting the effects of an intracorporeal surgical intervention applied to a body segment comprising at least one organ on the external appearance of the body segment, the method comprising implementing, by means of data processing on a server, the following steps: - step (a) obtaining a biomechanical model of the body segment; - step (b) applying at least one principal deformation to at least one part of interest of a portion of the model relating to the organ, said principal deformation being representative of a step of the intracorporeal surgical intervention relating to the part of interest; - step (c) applying an incident deformation to the model, excluding the part of interest, said incident deformation being a function of the principal deformation. Figure 2 for the abstract.

The application relates to a posture recognition and posture evaluation method based on a wearable device and relates to the field of human posture detection. The method solves the problems of strong subjectivity, insufficient data dimension and limited evaluation accuracy of traditional posture evaluation which depends on manual observation or a single sensor. The method is as follows: a plurality of parts of an inertial measurement unit and a flexible plantar pressure sensor array are deployed and synchronously collected, time synchronization and other treatments are carried out, time windows of each action are determined by taking a sudden change point of a plantar pressure sequence as an event anchor point, a fusion feature vector is constructed, a deep evaluation is carried out in combination with a fusion feature vector for identifying a motion posture and a preset biomechanical model, differences between an action of a wearer and a standard action or a personal action feature baseline are quantified, and finally a personalized improvement report is generated. The method can be applied to the fields of football, volleyball, tennis and other sports evaluation fields, the fields of posture evaluation, motion control and intelligent manufacturing of industrial robots and man-machine cooperation.

The invention provides a method and a system for designing an oral implanting operation guide plate, and belongs to the field of medical instruments, the method comprises the following steps: acquiring and preprocessing medical image data of a patient, and generating a three-dimensional digital model; mapping a Henry unit value in the medical image data into material mechanical properties of different positions of the skeleton to obtain a biomechanical model; on the basis of a biomechanical model, quantifying biomechanics, material strength and a restoration effect, defining the biomechanics, the material strength and the restoration effect as objective functions, and performing multi-objective optimization to generate an optimal implant restoration solution parameter set; generating a three-dimensional model of the implant restoration according to the optimal implant restoration solution parameter set; a surgical guide model is generated based on the three-dimensional model of the implant prosthesis and the patient dentition surface model in the three-dimensional digital model. In this way, the finally generated operation guide plate can better accord with the physiological structure and the mechanical environment of a patient, and therefore the precision and safety of an implantation operation and the long-term stability after repairing are improved.

This invention belongs to the field of dynamic simulation control, and specifically discloses a method, system, and application for human sagittal plane standing balance dynamic simulation based on center-of-mass delay feedback. The method includes: constructing a multi-joint biomechanical model of the human sagittal plane; constructing a joint torque generation model, where joint torques include passive and active torques; employing an active control strategy based on center-of-mass delay feedback, using the displacement and velocity deviations of the human center of mass as feedback state quantities, and introducing neural response delays to calculate the active joint torques; building a physical topology model corresponding to the biomechanical model in a physical simulation environment, connecting the joint torque generation model as a joint torque source to each joint to form a simulation model; inputting external disturbance displacement data into the simulation model, and identifying simulation model parameters based on simulated and real joint torques. This invention overcomes the limitations of traditional single-joint independent control, improving the fitting accuracy of multi-joint torques and the biorealism of collaborative modes.

The invention discloses a heart soft tissue model correction method and device based on a physical information neural network. The method comprises the following steps: acquiring a preoperative image to construct a personalized heart biomechanical model and an active movement prior library; constructing a double-flow coupling physical information neural network, wherein the double-flow coupling physical information neural network comprises an active deformation prediction branch used for predicting heart autonomous pulsation and a passive residual error correction branch used for correcting deformation caused by external contact force; defining a loss function including data matching loss and biomechanical control equation loss, and training the network; and receiving cardiac phase data and sparse surface displacement data collected in an operation, inputting the data into the trained network, and outputting a global three-dimensional deformation field to update the preoperative three-dimensional model. According to the method, active and passive mixed deformation of the heart is effectively decoupled through a double-flow coupling architecture, a physical control equation is embedded as a strong constraint, full-field volume deformation can be deduced only by using sparse surface observation points, and the problem of data sparsity is solved.