194 results about "Joints movement" patented technology

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

The invention belongs to the field of robot motion control, and discloses an online generation and control method and system for whole-body coordinated walking motion of a humanoid robot, a computer readable storage medium and a computer program product. According to the method, in walking motion generation, a whole-body walking motion track including upper body joint motion information is generated in real time by adopting a whole-body kinematics-centroid dynamics model; a model predictive control model is adopted to realize organic combination of motion trail generation and real-time tracking control; and solving the model prediction control model, and taking the optimal state x * and the corresponding control input u * as the input of whole body control. According to the method, in the planning stage, namely the whole-body walking motion trail including upper body joint motion information, the whole-body dynamic characteristics of the arms, the waist and the like are comprehensively considered, and a new method of long-time-domain trajectory optimization and online control is combined, so that real whole-body coordinated walking is fundamentally realized.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention provides an intelligent scoring method and system for joint movement. The method comprises the following steps: acquiring six-degree-of-freedom inertial motion data and surface electromyogram signals, and establishing a motion sequence topological structure containing a space-time reference based on a dynamic sampling strategy; secondly, decomposing inertial data into rigid body and physiological tremor components, and generating a three-dimensional biomechanical model containing joint rotation center drift, multi-plane coupling errors and trajectory curvature; secondly, constructing a time-varying mapping relation between the surface electromyogram signals and the exercise load, segmenting the signals based on phase mutation points, analyzing phase synchronism of an active muscle group and an antagonistic muscle group, and extracting a muscle cooperation mode; and finally, generating a motion feature tensor in a four-dimensional space-time coordinate system through technologies such as space-time coupling and a dynamic transfer function, analyzing an evolution mode of the motion feature tensor in real time to construct a scoring function cluster, and generating a composite scoring value. According to the technical scheme provided by the invention, the accuracy and reliability of joint movement quality evaluation are remarkably improved.

The invention discloses a joint rehabilitation effect evaluation method and system, electronic equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring video data of a target patient executing a preset rehabilitation action; identifying a joint position coordinate point from the video data; a joint movement track is constructed according to the time sequence of the joint position coordinate points, track parameters of the joint movement track are calculated, and the track parameters comprise the movement angle, the track curvature and the symmetry degree; acquiring a speed change sequence and an acceleration change sequence of joint movement in the movement track, and determining a joint rehabilitation index of the target patient based on the track parameter, the speed change sequence and the acceleration change sequence; and comparing the joint rehabilitation index with a reference index corresponding to the preset rehabilitation action, and determining the joint rehabilitation progress of the target patient. By implementing the technical scheme provided by the invention, the effect of improving the evaluation accuracy of the joint rehabilitation effect is achieved.

The invention discloses a knee bone joint state recognition method and system based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the steps: obtaining multi-source medical image data of a knee joint region of a patient, carrying out the spatial registration and standardization preprocessing, and constructing a three-dimensional structure model of a knee joint after unifying a coordinate system; establishing a mapping relation between joint movement and an anatomical structure by combining knee vibration and behavior characteristic data collected by a patient in a specific movement state, extracting joint key geometric and mechanical parameters from the three-dimensional structure model, and fusing the joint key geometric and mechanical parameters with synchronously collected vibration behavior characteristics; and performing state recognition on the fused multi-source features based on an intelligent recognition model, and outputting a health state label of the knee joint. According to the invention, more accurate and more comprehensive knee joint health state identification can be realized, the accuracy and reliability of an identification result are effectively improved, and the timeliness of disease discovery and the scientificity of intervention are enhanced.

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 provides a humanoid robot joint life monitoring method, and belongs to the technical field of life monitoring. The method comprises the steps that 1, a multi-mode sensor array is constructed to collect original joint motion data of a target robot; 2, standardizing the original joint movement data according to feature types to obtain standard feature data of all parameter types; 3, establishing a joint degradation model of the target robot based on the standard feature data; 4, inputting real-time motion data of the target robot into the joint degradation model, and outputting a joint residual life index; and 5, when the residual life index of the joint is lower than a preset threshold value, a self-adaptive maintenance strategy is triggered. Through real-time monitoring of joint health, early warning and optimization maintenance, the reliability of the robot is improved.

The application relates to a multi-arm mobile robot control system, which comprises a robot, each joint position of the mechanical arm of which is configured with a joint controller for driving joint movement; a robot controller for realizing the operation of joint motors according to control quantities; a joint position hard analysis unit for calculating the coordinate positions of each joint or mechanical arm in a space coordinate system to output results; and a joint space point position trajectory constraint model for forming a correction quantity of a joint and superimposing the correction quantity on an original control quantity of the joint to become a final control quantity for controlling the joint, the final control quantity being used for output to the joint controller to realize joint movement. The application provides a multi-arm mobile robot control system, which effectively realizes the cooperative control among multi-arm robots, avoids the mutual collision among mechanical arms, improves the avoidance and dynamic response speed of the mechanical arms, and improves the agility of robot actions.

The invention discloses a mechanical arm state limited preset time point control method and system, and relates to the technical field of robot control, and the method comprises the steps: obtaining a joint position tracking error and a joint speed tracking error based on a joint motion state and an expected trajectory of a mechanical arm; based on constrained system states corresponding to the joint position tracking error and the joint speed tracking error, nonlinear transformation is carried out on the joint position tracking error and the joint speed tracking error through an unconstrained state function, and an equivalent unconstrained virtual error variable is constructed in combination with coordinate transformation; based on the unconstrained virtual error variable, designing a sectional sliding mode variable; and designing a second-order reaching law based on the sectional sliding mode variable to form a closed-loop dynamic state, and solving based on the closed-loop dynamic state to obtain a control torque so as to control the mechanical arm joint. According to the method, the trajectory tracking error of the mechanical arm can be converged at the moment specified by a user, the system state is ensured to meet strict physical constraints in the whole process, and the method can be applied to accurate trajectory control in a complex task scene.

The invention relates to the technical field of electric digital data processing, in particular to an intelligent early warning method and system for joint rehabilitation medical equipment, and the method comprises the steps: obtaining the heart rate and joint motion resistance of a user at a plurality of historical moments and in the rehabilitation training process at the current moment; fitting based on the joint movement resistance of the user at multiple historical moments to obtain a resistance safety baseline, and determining a resistance threshold value according to a mean value and a standard deviation of the resistance safety baseline within a set time; if the heart rate of the user at the current moment exceeds a set first threshold value or the joint movement resistance of the user at the current moment exceeds a resistance threshold value for a preset duration, triggering an alarm and stopping rehabilitation training. According to the invention, the problem that self-adaptive switching and intensity optimization adjustment of training modes cannot be realized is solved.

The present invention discloses a key joint motion estimation based method for estimating continuous human postures. A motion estimation block matching algorithm is applied to human key joint tracking, so as to obtain continuous human posture results. Meanwhile, the results are continuously corrected by using a deep neural network based human posture estimator. The present invention may estimate the continuous human postures in a video stream, where the human postures are specifically embodied as coordinate positions of human joints in a video frame. Compared with a posture estimation method completely relying on a deep neural network, the posture estimation method provided by the present invention has the advantages of high frame rate, low hardware requirements, and sequential continuity of recognition results; and compared with a posture estimation method completely relying on a motion estimation algorithm, the present invention may correct a cumulative error, to improve the estimation accuracy.

The invention provides a multi-scene body position conversion-based standing-up assisting control method and system for the elderly suffering from stroke. According to the method, firstly, the standing-assisting device is controlled to obtain a fusion sensing result composed of body pressure distribution data and joint movement data, multi-feature coupling analysis is carried out to generate a combined recognition conclusion containing a body position state and a scene type, and then dynamic mode mapping is carried out according to the combined recognition conclusion. Outputting a target control mode matched with the current scene type and the body position, and finally performing power instruction synthesis by utilizing the target control mode to generate a cooperative control signal for driving the standing assisting equipment to execute body position conversion; according to the technical scheme provided by the invention, accurate conversion from standardized control to personalized control is realized, the suitability and the user experience of the standing assisting process are improved, the uncertainty and the individual difference in actual operation are overcome, and the stability, the safety and the adaptability of the body position conversion process are improved.

The invention relates to the technical field of data processing, in particular to a rehabilitation training control method and system based on a flexible hand joint rehabilitation glove, and the method comprises the steps: obtaining an individualized physiological parameter baseline of a patient; real-time data of a patient in the rehabilitation training process are collected through a multi-modal sensor array on the flexible glove, the active participation degree, the joint movement smoothness and the muscle fatigue degree of the patient are calculated based on the real-time data, the active movement intention of the patient is recognized, and a real-time rehabilitation state evaluation result of the patient is obtained; generating a control instruction through an adaptive control engine according to a preset personalized rehabilitation scheme and the real-time rehabilitation state evaluation result; the generated control instruction is sent to a driving control circuit so as to control a driving unit to execute corresponding bending or stretching actions and assist the patient in completing target training actions; according to the invention, personalized, self-adaptive and quantitative evaluation of rehabilitation training can be realized, and the efficiency and effect of hand rehabilitation training are improved.

The invention relates to the technical field of rehabilitation training, and particularly discloses a method, system and device for assisting walking training through visual and auditory stimulation. Visual and auditory data is generated by means of auxiliary training equipment to guide a user to walk and train, parameters are adjusted according to the body state of the user, limb coordination characteristics and muscle group coordination characteristics are obtained according to the multiple pieces of joint movement track information, gait parameters are calculated according to the limb coordination characteristics and the muscle group coordination characteristics, and the gait training accuracy is improved. The method comprises the following steps: acquiring accurate limb gait information, then judging whether the limb gait information meets a preset condition or not, if not, generating posture auxiliary information according to an analysis result, adjusting own parameters of auxiliary training equipment according to the posture auxiliary information, and re-acquiring joint movement track information; if yes, equipment parameters are fixed, a complete and scientific home rehabilitation training system is formed, and it is ensured that training is stable and coherent.

The invention relates to the technical field of medical simulation training, in particular to a simulation live wire treatment training method and system for high-risk orthopedic wounds, and the method comprises the following steps: calling preset explosion shock wave parameters, calculating the influence of shock waves on bones of multiple parts in combination with the stress-strain characteristics of bone tissues, and analyzing the mechanical response of the bone tissues, and simulating fracture injury conditions to obtain injury simulation data. According to the invention, through refined simulation of fracture injury and fracture complications, injury condition data are close to a real battlefield environment, students can visually understand dynamic evolution of wounds, and by collecting joint movement data of the students in real time and combining mechanical response of fracture parts, direct influence of treatment actions on the wounds is analyzed, so that the treatment effect of the wounds is improved. In the training process, wound state mapping is adjusted in real time, students are helped to understand operation feedback in real time, in combination with assessment of treatment skills, the skill level of the students is helped to be understood, a data basis is provided for adjusting training courses, and the skill training effect and efficiency are improved.

The invention relates to the technical field of robots, and particularly discloses a bionic humanoid mechanical arm which comprises a shoulder joint module, a big arm module, an elbow joint module, a small arm module and a wrist joint module which are connected in sequence. The wrist joint module realizes two-degree-of-freedom flexible movement through a regular octahedron tensegrity structure, and the rotation centers of the wrist joint module naturally intersect at one point and are connected with the rolling shaft in series to form three-degree-of-freedom concurrent rotation; the length-adjustable supporting rod in the flexible structure changes the joint rigidity in real time and dynamically adapts to the load change so as to enhance the man-machine interaction safety; an elbow joint adopts a virtual pitch circle rolling mechanism, all joint-crossing driving rope paths are forced to remain unchanged, and multi-joint motion coupling is eliminated from the mechanical intrinsic level; a driving motor and a rope wheel system are arranged on the large arm module in a centralized mode, the rope tensioning state is continuously optimized in cooperation with a ratchet wheel type self-locking tension mechanism, and the tail end inertia is reduced under the condition that an elbow and wrist joint motor is completely removed.

The invention provides a virtual reality-based joint movement injury rehabilitation mode generation method and a virtual reality-based joint movement injury rehabilitation mode generation system, and relates to the technical field of virtual reality, and the method comprises the following steps: analyzing a rehabilitation path according to movement joint injury data of a user; performing rehabilitation limb posture decomposition based on the rehabilitation analysis path to generate a rehabilitation analysis view; a multi-modal sensor unit is used for collecting rehabilitation feedback actions of a user, and matching recognition of physiological parameters and rehabilitation constraints is carried out in a rehabilitation analysis view; and according to the posture deviation feedback, performing amplitude and muscle power guidance feedback of the rehabilitation limb posture, and synchronously performing virtual reality visual display. The technical problem that in the prior art, most VR rehabilitation systems mainly pay attention to visual feedback, so that only the motion appearance can be evaluated, and the compensatory motion mode cannot be recognized is solved, real-time monitoring and guidance on the force exerting correctness are achieved through combination of posture decomposition and physiological parameter projection, and the effect that the correctness of force exerting is improved is achieved. Therefore, the compensatory motion mode is effectively blocked.

The invention provides a control method and device of a mechanical arm, a robot and a storage medium, and relates to the technical field of robotics.The method comprises the steps that according to the actual reachable range of at least one mechanism joint of the mechanical arm and the target pose expected to be reached by the tail end of the mechanical arm, the target pose is determined; determining an expected joint position of at least one mechanism joint under the target pose; and motion control is conducted on the mechanical arm, so that at least one mechanism joint moves to the corresponding expected joint position. Therefore, when the inverse kinematics of the mechanical arm is calculated, the expected joint position of each mechanism joint is determined according to the actual reachable range of at least one mechanism joint and the expected target pose of the tail end, so that the expected joint position of each mechanism joint can be accurately calculated; the deviation between the calculation result and the actual movement condition is greatly reduced, the movement potential of each mechanism joint can be fully utilized, the movement range of the tail end of the mechanical arm is effectively expanded, and the mechanical arm can reach more poses.

The invention discloses a human body action recognition method and system based on a skeleton spatial-temporal feature fusion graph convolutional network, and relates to the field of computer vision, the method comprises the following steps: inputting a to-be-detected video to a posture estimation algorithm to obtain a human body skeleton data set of the to-be-detected video; performing data preprocessing on the human skeleton data set to obtain skeleton joint information flow, skeleton joint movement information flow, skeleton information flow and skeleton movement information flow, and fusing the information flows to form a double-flow network branch; respectively inputting skeleton joint information flow-skeleton joint motion information flow branches and skeleton information flow-skeleton motion information flow branches in the double-flow network branches into a skeleton spatial-temporal feature fusion graph convolutional network model to obtain double-flow network branch output; and on the basis of double-flow network branch output, obtaining a human body action prediction result of the video to be detected by adopting a weighted fusion method. According to the invention, the recognition precision and robustness of complex actions can be effectively improved.

The invention relates to the technical field of path optimization, in particular to a multi-axis robot path planning optimization method and system, and the method comprises the following steps: collecting the current position and the start-stop interval of a multi-motion-axis joint, calculating the motion distance of each joint, converting the motion distance into an interval occupation ratio, and generating an initial path point through the interpolation of the current position; the method comprises the following steps of: selecting a plurality of path point joint combinations, performing occupation proportion comparison on the plurality of path point joint combinations to determine a dominant constraint direction, performing mapping cutting on other joint motion ranges according to a residual interval proportion, screening path points meeting constraint conditions, and verifying path sequence consistency and joint displacement continuity to form an optimized motion path of the robot. Through multi-motion-axis joint interval proportional description, an interval occupation state is introduced to participate in path screening, a dominant constraint direction is determined, and other joint intervals are mapped and cut, so that multi-axis motion proportions are coordinated, invalid displacement is reduced, and collaboration and motion smoothness are improved.

The disclosure related to methods and systems for image data analysis for objectively analyzing joint motion data. In one embodiment, the methods and system disclosed herein use automated methods to capture image data, extract motion data, and determine an objective score for one or more joints in one or more motions. The system and methods disclosed herein comprise a motion capture system with one or more image capture devices to obtain motion data for a subject and using one or more regression models and analysis techniques, various objective scores can be generated related to the subject's joint motion.

The present application provides a joint motion data adjustment method, device and electronic device, which perform dynamic correction on the joint motion data based on spring-damper system modeling, thereby achieving smooth denoising of the joint motion data while well simulating real human body movement, so that the adjusted joint motion data has a higher degree of naturalness when driving the virtual model to move.

The invention relates to the technical field of robots, and discloses a human body tumble protection method and device, an exoskeleton robot and a medium, and the method comprises the steps: obtaining multi-modal motion data, collected by a sensor in real time, of a user; inputting the multi-modal motion data into a tumble identification model for identification and classification to obtain a tumble type probability of the user; if the tumble probability is greater than a preset safety threshold, determining that the user is in a tumble trend, and generating a variable impedance control instruction matched with the target tumble type according to a preset strategy library; and determining a rigidity parameter and a damping parameter of a target joint motor according to the variable impedance control instruction, and determining a target output torque based on the joint motion data, so that the target joint motor performs fall protection on the user based on the target output torque. The fall type can be accurately recognized, flexible buffering can be achieved, and then the injury risk of a user is remarkably reduced.

An embodiment of the present application provides a joint error optimization control method, a controller, and a minimally invasive robot. The method includes: during the movement of the robotic arm, the controller can obtain the real-time position and real-time speed of the basic joint on the robotic arm in real time. In addition, the controller can also obtain the relative angle information of the basic joint on the robotic arm. The controller can analyze based on the real-time position, real-time speed, and relative angle information of the basic joint to determine the movement trajectory of the redundant joint on the robotic arm during the follow-up process during the movement of the basic joint. Furthermore, based on this movement trajectory, the operation instruction of the redundant joint can be determined, and the redundant joint can be controlled to move according to the movement joint to achieve the follow-up of the redundant joint. This method is used to improve the optimization effect of the joint movement trajectory.

This invention relates to a humanoid robot, specifically a parallel rotating mechanical joint based on a ring-shaped guide rail, mimicking the rotation of a human neck. The aim is to provide a parallel rotating mechanical joint based on a ring-shaped guide rail to achieve three degrees of freedom in the movement of a biomimetic human neck joint while ensuring high rigidity and motion accuracy. Furthermore, the joint features a simplified structure, good support performance, and sufficient movement space for the neck joint. The technical solution is a parallel rotating mechanical joint based on a ring-shaped guide rail, characterized by: a head motion platform, a neck ring-shaped guide rail base, and three PSS (Personal Stability Support) link branches connected in parallel between the head motion platform and the neck ring-shaped guide rail base; the center of the head motion platform and the center of the neck ring-shaped guide rail base are connected by a composite rotating shaft.

The embodiment of the utility model discloses a hip joint motion simulation device, which comprises a crotch back cushion, a first supporting plate, a first mounting plate and a first driving mechanism, and is characterized in that the crotch back cushion, the first supporting plate and the first mounting plate are sequentially and oppositely arranged along a first horizontal direction, and the crotch back cushion is mounted on the first supporting plate; the middle of the supporting plate is hinged to the first mounting plate through a first rotating shaft arranged in the vertical direction, the first driving mechanism is arranged on the first mounting plate, the driving end of the first driving mechanism is movably connected with the first supporting plate through a connecting rod, and one end of the connecting rod is hinged to the driving end of the first driving mechanism; the other end of the connecting rod is hinged to one end of the first supporting plate, and the first driving mechanism is used for linearly driving the first supporting plate to rotate around the first rotating shaft in the first horizontal direction so as to drive the crotch back cushion to swing front and back. According to the embodiment of the invention, the hip joint of the patient can be driven to simulate the motion state when the lower limbs walk.

Systems and methods for providing assistance with human motion, including hip and ankle motion, are disclosed. Sensor feedback is used to determine an appropriate profile for actuating a wearable robotic system to deliver desired joint motion assistance. Variations in user kinetics and kinematics, as well as construction, materials, and fit of the wearable robotic system, are considered in order to provide assistance tailored to the user and current activity.

The invention discloses a rehabilitation treatment evaluation method and system based on wearable equipment, and relates to the technical field of medical rehabilitation. The system comprises a wearable sensing module, a wireless communication module, a cloud data processing module, a man-machine interaction module and an auxiliary stimulation module. The wearable sensing module collects joint movement, muscle movement and stress parameters in a non-invasive mode. According to the method, through closed-loop processes of equipment preprocessing and personalized configuration, adaptive model training, real-time data acquisition, cloud quantitative evaluation and multi-dimensional feedback adjustment, full-chain coverage of'perception-analysis-evaluation-feedback 'of rehabilitation treatment is realized. According to the method, the defect that a cognitive movement therapy lacks quantitative indexes is overcome, the problems that traditional rehabilitation evaluation is subjective and a sensor can only detect and cannot assist in treatment are solved, rehabilitation treatment standardization, individuation and high efficiency are achieved, the labor cost is reduced, the rehabilitation quality is improved, and the method is suitable for rehabilitation scenes such as cerebral apoplexy and movement injury.

The utility model discloses a simulation dynamic performance monkey robot device which comprises a neck assembly which is arranged on a base trunk assembly and can twist and pitch, a head assembly which is arranged on the neck assembly and can shake heads and nodding heads, and the head assembly is provided with an opening and closing mouth component and a blinking eye component. The left arm assembly and the right arm assembly are connected to the two sides of the base trunk assembly and can swing front and back, twist big arms and bend elbows, and all the assemblies are wrapped with monkey skin models. The device is simple and ingenious in overall structure and reliable in performance, multi-degree-of-freedom and multi-joint movement can be achieved, and the problems that in the performance process of simulation primates, joints are stiff, actions are stiff, and the expressive force is insufficient are solved.