769 results about "Injury risk" patented technology

The invention discloses an intelligent network connection automobile safety control method and system based on active and passive safety information fusion. The system comprises an environment detection module, a risk prediction module and a cooperative control module. Wherein the environment detection module comprises a passenger state monitoring part, a vehicle system monitoring part and a traffic environment sensing part, the risk prediction module comprises a vehicle collision risk prediction part and a passenger injury risk prediction part, and the cooperative control module comprises an in-vehicle intelligent restraint system, a vehicle steering system and a control execution part of a braking system. The vehicle collision risk probability and the passenger injury probability are calculated in real time according to the established vehicle risk prediction model and the passenger injury risk prediction model by fully utilizing the early warning capability of an active safety system for potential risks. When the system encounters unavoidable dangerous working conditions, the system adjusts the state of the restraint system to enable the restraint system to reach the optimal state of current passenger protection, and extra risks, caused by non-cooperation of the active safety system and the restraint system, of passengers are avoided.

The invention relates to the technical field of data analysis, and discloses a data analysis system for multi-joint exercise rehabilitation monitoring. The system collects three-dimensional motion data, myoelectricity activity signals and spatial position data of multiple joints in real time through a multi-mode sensor array. The system comprises a data acquisition module, a preprocessing module, a space-time analysis module and a result output module. The preprocessing module is used for carrying out noise removal, normalization and feature extraction on the original data; the space-time analysis module constructs a monitoring model based on a graph space-time network, and analyzes space-time characteristics of multi-modal data through dynamic joint relation modeling by adopting an intelligent chip. And the result output module generates a visual report including exercise coordination evaluation, injury risk prediction and rehabilitation progress judgment, and feeds back the visual report to the rehabilitation equipment in real time. Besides, the system also has distributed storage and real-time feedback functions, the accuracy and efficiency of rehabilitation monitoring are improved, and the problems of single data processing and inaccurate spatial-temporal feature capture in the prior art are solved.

The invention discloses a hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment, relates to the technical field of medical rehabilitation, and discloses the hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment. The training plan is dynamically adjusted in combination with multi-dimensional indexes, the problems of compensation recognition deficiency, feedback lag and plan stiffness in a traditional rehabilitation scheme are effectively solved, the rehabilitation effect can be improved, and the injury risk of adjacent joints can be reduced.

The invention relates to the technical field of vehicle safety, in particular to a pedestrian body model chest injury risk curve adjusting method. Comprising the following steps: analyzing the relationship between the speed and the chest injury risk, and drawing a first curve between the vehicle speed and the injury risk based on accident statistical data; fitting data of a rib cortical bone uniaxial stretching material mechanics experiment; constructing a vehicle-pedestrian side impact simulation experiment matrix under different working conditions, and generating a simulation sample number; extracting the rib strain of the human body model in the sampling model, calculating the fracture probability of a single rib of the chest of the human body model, and then calculating the maximum simple injury probability of the specified chest of the human body model; fitting the relationship between the vehicle speed in the simulation model and the chest injury risk of the human body model, and drawing a second curve between the vehicle speed in the sample and the chest injury risk of the pedestrian model; and constructing an objective function according to the difference between the first risk curve and the second risk curve. According to the technical scheme, the accuracy of accident damage prediction can be improved.

The invention discloses a radiofrequency ablation navigation device based on multi-modal image fusion. The device comprises a respiration sensor, an image reconstruction server, an interaction terminal and a processor. The respiration sensor is worn on a target object and used for collecting respiration data of the target object. The image reconstruction server is used for receiving the multi-modal image of the target object and reconstructing the multi-modal image into a three-dimensional model; the three-dimensional model comprises an organ geometric model, nerve distribution and a blood vessel network. And the interaction terminal is connected with the image reconstruction server and is used for marking a starting point and an ending point for the three-dimensional model. The processor is connected with the respiration sensor, the image reconstruction server and the interaction terminal; and the processor generates a predicted path output according to the respiration data and the three-dimensional model marking the starting point and the ending point. The method has the advantages that respiratory movement errors are accurately compensated, the nerve mistaken injury risk is reduced, and the operation safety is improved.

The invention relates to the field of swimming physical training analysis, and particularly discloses a swimming underwater physical training effect analysis system based on training data, which is characterized in that video data, motion data and physiological data of a swimmer are acquired by integrating an underwater camera, an inertial measurement unit, a myoelectricity sensor, a heart rate sensor and a blood oxygen sensor; space-time synchronous analysis of multi-modal data is realized; on the basis of joint angle change consistency and muscle activation synchronism, whether the action of the swimmer is standard or not is judged, AR real-time feedback is conducted, and real-time correction of the swimmer in training is facilitated; the training load of the swimmer is evaluated based on the underwater speed, the stroke, the stroke frequency, the swimming distance and the swimming duration, and overrun early warning is carried out, so that making of a personalized training plan is facilitated, and meanwhile, overtraining is avoided; by monitoring the heart rate, the oxyhemoglobin saturation, the muscle activation duration and the joint activity condition of the swimmer, whether the swimmer has the injury risk or not is judged, early warning is triggered, and the injury occurrence rate is reduced.

The invention relates to the technical field of pipeline conveying, and discloses an automatic conveying mechanism for welded pipe machining, which comprises a main body, a sliding space is arranged at the top of the main body, and the automatic conveying mechanism further comprises a conveying mechanism, and the conveying mechanism is arranged in the sliding space and is used for conveying pipelines. According to the pipeline placing device disclosed by the invention, the pipeline can more easily slide into or be placed on the placing assembly to be conveyed through inclination of the shaking frame during pipeline placing while a traditional placing process is simplified through inclined conveying of the shaking frame under the weight of the pipeline during initial bearing of the pipeline, so that the requirements of manual adjustment and intervention are reduced; the labor intensity of workers is relieved, the fatigue and injury risks caused by long-time lifting are reduced, and therefore the smoothness and efficiency of the conveying process are improved.

The invention relates to the technical field of medical rehabilitation, and discloses a multi-modal data fusion and real-time monitoring method and system for orthopedic rehabilitation, and the system comprises a multi-modal data collection module, a data fusion processing module and a real-time monitoring execution module. The system is provided with a multi-modal feature calibration module, when the rehabilitation state of an orthopedic patient is dynamically evaluated, the system aligns the time sequence difference between a motion track and muscle fatigue features in real time by deploying a feature fusion model based on an attention mechanism, and joint load analysis distortion caused by multi-modal data delay can be corrected in a millisecond level; the accuracy of rehabilitation risk assessment is ensured, the joint secondary injury risk is reduced, an environment coupling risk identification unit is arranged, when abnormal action active early warning is carried out, the joint motion range and environment factors are dynamically associated through a reinforcement learning algorithm, the gait safety threshold value under the ground wet and slippery condition is corrected in real time, and the joint secondary injury risk is reduced. And the safety and the response real-time performance of the rehabilitation process are ensured.

The invention provides a large-angle zero-gravity seat damage risk assessment method, and the method comprises the following steps: S1, collecting the pressure and comfort data of a dummy at different angles and under different supporting point configurations through an experiment, and building a dummy simulation model suitable for a large-angle zero-gravity seat; s2, on the basis of the dummy simulation model of the large-angle zero-gravity seat, vehicle collision and impact tests are added, acceleration and impact force data under different seat adjusting angles are obtained, a large-angle zero-gravity seat passenger injury prediction simulation model is established, and therefore a injury prediction value is obtained; and S3, calculating a damage risk score according to the damage predicted value.

The invention relates to a personalized exercise rehabilitation risk early warning method and device based on IMU dynamic instability assessment, and the method comprises the steps: collecting multi-dimensional exercise data of a subject, carrying out the posture fusion, and obtaining the limb posture, joint angle change rate and angular momentum of the subject; performing time sequence mode decomposition to extract a single gait cycle and a specific jump landing attitude; calculating a biomechanical feature vector of each motion primitive; inputting the single gait cycle, the specific jump landing attitude and the biomechanical feature vector into a hybrid injury induced motion model, and outputting a multi-dimensional injury risk index vector; and comparing the multi-dimensional injury risk index vector with historical health data and a moving target dynamic adjustment threshold value of the subject, and if the threshold value is exceeded, providing real-time auditory, tactile or visual feedback to the subject. According to the method, the influence of movement speed and rhythm change is overcome, dynamic instability in movement can be more sensitively reflected, and personalized risk assessment in a real sense is realized.

The invention discloses an efficient metal bottom cover stamping device for an automobile safety air bag, and relates to the technical field of safety air bag metal bottom cover production. The three fixed extrusion blocks are used for limiting and fixing the metal bottom cover material plate above the stamping lower die, so that the stamping positions of the stamped gas guide hole, the air bag expansion hole and the riveting hole are accurate, the stamping efficiency of the metal bottom cover stamping machine is greatly improved, and compared with the prior art, the stamping efficiency of the metal bottom cover stamping machine is greatly improved. The accuracy of the air bag expansion hole, the air flow guide hole and the riveting hole is improved, the accuracy of the air bag expansion hole ensures that the air bag can be rapidly and effectively inflated during collision, the injury risk of passengers is reduced, the overall safety is improved, the accuracy of the air flow guide hole ensures that air flows smoothly, the air inflation speed of the air bag is increased, and the reaction capacity of the air bag is enhanced. And the accurate hole positions enable the assembly and maintenance processes to be more convenient.

The present invention provides a vertical, flexible, fully automatic press-fitting device for a motor stator base and rear end cover, belonging to the technical field of motor automatic press-fitting equipment. The device comprises a device body, an automatic assembly line disposed within the device body, both ends of the automatic assembly line passing through the device body, a robot disposed on the side of one end of the automatic assembly line, a product pallet carrier mounted on the upper end of the automatic assembly line, a lifting and unloading mechanism mounted in the device body, a stator base positioning and transplanting mechanism disposed at the left end of the device body, and a Z-axis press-fitting mechanism disposed at the upper end of the device body. The present invention solves the problem that the existing assembly of the motor stator base and rear end cover requires manual loading and unloading of materials, resulting in low efficiency, waste of human resources, and the risk of injury to workers during handling.

The invention relates to the technical field of artificial intelligence, and particularly provides a school student exercise injury prediction system based on an AI large model. The system comprises a data collection module, a data integration and preprocessing module, an AI prediction model module, a real-time monitoring and alarm module, a user interaction module and a feedback and optimization module. Original data flows into the data collection module from multiple sources, passes through the data integration and preprocessing module and then is input into the AI prediction model module, and an injury risk prediction result is generated; the real-time monitoring and alarm module gives an alarm according to a risk prediction result, the user interaction module provides feedback, the feedback and optimization module is used for updating an AI prediction model, and the system improves the accuracy of exercise injury prediction of students at school.

The invention discloses a real-time industrial injury risk assessment and early warning method based on multi-modal data fusion. The method comprises the following steps: firstly, respectively extracting static numerical type, static category type and multi-modal time series data, such as features of text and image data, then aligning and fusing the data to generate efficient representation of the multi-modal data, then training a prediction model off line, deploying the model on line for real-time risk assessment, and finally carrying out real-time risk assessment. In order to adapt to continuously updated data streams, model parameters are optimized through an online incremental learning technology, and the accuracy of the model prediction amount is ensured. And finally, determining whether an early warning is given or not through prediction data of the prediction model within a period of time. According to the method, multi-modal data features are fused, model parameters are updated through online incremental learning, and the precision of model prediction is ensured. The industrial injury risk is effectively evaluated, and a corresponding early warning mechanism is triggered according to the evaluation result, so that an enterprise is helped to take precautionary measures in time, and the industrial injury risk is reduced.

The invention provides an intraoperative neurovascular bundle identification and injury early warning method and system based on artificial intelligence. According to the method, real-time image data are acquired through a multi-modal imaging device, multi-scale blood vessel features are extracted by adopting a three-stage cascade adaptive path convolution (APC) module, the long-range topology perception capability is enhanced in combination with a local-global interaction module (LGIM), global features are integrated by utilizing a cross-scale deformable convolutional network (CSDCN), and dimensions are compressed through a multi-branch feature distillation module (MFDM). And the multi-task classifier synchronously outputs the category, position and injury risk assessment of the blood vessel bundle, and real-time multi-modal early warning is realized through dynamic confidence threshold and mahalanobis distance anomaly detection. According to the invention, the accuracy and real-time performance of intraoperative nerve and blood vessel protection are obviously improved.

The utility model relates to the technical field of riding, and discloses a riding helmet with a safety air bag, which comprises a helmet main body, the lower edge of one end of the helmet main body is provided with an extension part, the outer side of the extension part is provided with a mounting groove, an air bag bin is mounted in the mounting groove, an air pump bin is mounted above the air bag bin, and the air pump bin is connected with the air bag bin. An air pump body is arranged in the air pump bin, a trigger device is installed at the end of the air pump body, and an air bag located in the air bag bin is installed on the trigger device. Through cooperation of the helmet body, the air pump bin, the air pump body, the air bag bin and the trigger device, the gravity center change of a rider is detected through the trigger device, and then the air pump body is started to inflate an air bag in the air bag bin, so that the head and neck of the rider are protected after the air bag is unfolded, and then the situation that the rider is hurt due to an emergency situation is avoided; guarantee is provided for the safety of a rider, and the injury risk is reduced.

The invention provides an intelligent motion evaluation and damage early warning method and system based on multi-source biological signal fusion, and is applied to the technical field of medical data processing. The method comprises the following steps: acquiring a multi-modal biological signal acquired based on a multi-source sensing node; carrying out self-adaptive preprocessing on the multi-modal biological signals, eliminating motion artifacts by adopting an improved spectral subtraction formula, realizing multi-rate synchronization through a Lagrange kernel, triggering node vibration by virtue of signal quality evaluation to prompt electrode reattachment, and generating preprocessed signal features; and constructing cross-modal fusion features by using multi-scale cavity convolution and the like, and outputting results such as a motion mode and the like. A target depth model is trained in combination with signal optimization and data enhancement and the like, a motion pattern classification result, joint load estimation data and a damage risk scoring result are processed based on the target depth model, damage early warning information is generated, and accurate and dynamic motion evaluation and damage early warning are achieved.

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

A game controller component configured for controlling an electronic game. An improved thumb actuated user input enables greater performance, increases ergonomics, and offers a new tactile input option. The improved thumb actuated user input includes various components to make possible an input experience resembling a centering trackball, with physical user feedback further augmenting user performance. Other inputs may be featured at various other areas of the controller to further increase these capabilities. Placement of individual inputs may rely on certain human factors, such as natural hand placement over a desk surface or lap. As the user activates various input components, including the thumb actuator(s), data, messages, and instructions are provided to the computing device to cause electronic game movements or actions and users benefit from various arrangements by increasing their speed, comfort, and accuracy while decreasing fatigue and injury risk.

The subject innovation is directed to an exercise training device and methods for use thereof, and in particular, an experiential self corrective neuro-muscular training device designed to engage and facilitate the stability and movement in all three anatomical planes in training and exercising primary functional movement patterns for the purpose of improving ones biomechanics and reducing the risk of injury.

The invention provides a throwing action real-time guidance method based on causal inference and self-adaption, and the method comprises the steps: synchronously collecting the kinematics and electromyography data of an athlete through a multi-mode sensor, constructing and applying a dynamic causal graph model which integrates biomechanical priori knowledge and online self-adaption capability, processing the data in real time to generate deep causal enhancement state representation; and based on the model, performing autoregressive pre-judgment on the future motion trajectory of the athlete, and performing quantitative evaluation on the pre-judged trajectory by using a multi-objective cost function integrating athletic performance, injury risk and technical style. When the evaluation cost exceeds an intervention threshold value, a risk source is traced back to determine a kinematic error, and an error correction instruction is generated according to a preset error-instruction mapping relation; the fatigue state of an athlete is monitored in real time, model parameters are finely adjusted on line, a correction instruction is transmitted to the athlete in real time, and prospective and personalized intelligent guidance is achieved.

A system and method for monitoring the health and injury risk of players of a sport, using image and sensor data from sensors worn by the players. An injury risk score may be determined for each of the camera data and the sensor data. The data is then correlated to determine a risk score that may comprise a likelihood of the player having sustained an injury or the player's susceptibility to sustaining a future injury.

The invention discloses a method and a device for adjusting a cushion of a spinning and electronic equipment, and relates to the technical field of software and the field of sports science and technology. The method comprises the steps that under the condition that user authorization is obtained, user data of a user and riding exercise data when the user carries out riding training on the spinning bike according to a target course are obtained; a cushion of the spinning is adjusted to be in an initial state according to the user data; according to the course data of the target course and / or the riding motion data, in the process that the user uses the spinning bike to conduct riding training, the cushion in the initial state is adjusted. The technical problems that in the prior art, due to the fact that a unified cushion is generally provided for helping a user to conduct spinning riding training, the cushion cannot be adjusted in a personalized mode, the training quality and the training experience are affected, and even the training injury risk of the user is likely to be high are solved.

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and the method comprises the steps: collecting urban environment data, and obtaining the building distribution, public facility arrangement, average pedestrian flow and other information in a city; and respectively calculating an obstacle risk, an injury risk, a property loss risk and a weather influence existing in the urban environment, and finally obtaining a path of the unmanned aerial vehicle through an optimization algorithm. The method provided by the invention comprehensively considers the risks of obstacle collision, personal injury, property loss, weather influence and the like possibly caused by the operation of the unmanned aerial vehicle in the urban environment, is convenient to calculate, facilitates measurement or estimation of parameters, can quickly determine the path scheme of the unmanned aerial vehicle, and improves the working efficiency. Technical guarantee is provided for safe work of the unmanned aerial vehicle in a complex urban environment, energy conservation, improvement of the service life and improvement of the working efficiency.

The invention relates to a washing and caring machine in the technical field of bathing and caring, the washing and caring machine comprises a washing and caring device and a water treatment system, the washing and caring device comprises a cabin body and a spraying piece, and the spraying piece is arranged in the cabin body; the water treatment system is used for treating water to enable parameters of the water to meet preset conditions and conveying the treated water to the spraying part; when the spraying piece outputs water into the cabin body, a washing and protecting effect can be achieved; wherein the parameters of the water comprise at least one of temperature and cleanliness, and the communication relation among the water treatment system, the cabin body and the spraying piece is designed, so that the washing and protecting machine can achieve highly-automatic bathing operation from water inlet to water outlet, and a user can start bathing only by entering the cabin body; the user does not need to carry out tedious and complex pre-bath preparation work, and does not need to carry out large-amplitude carrying, lifting, moving and other operations on the user, so that the injury risk of the user in the moving process is greatly reduced, and the user experience is improved.

The invention discloses an exercise management method and system for musculoskeletal chronic pain, and relates to the technical field of exercise management rehabilitation, and the method comprises the steps: building a biomechanical model for a patient through finite element analysis software, determining an optimal exercise mode according to the pain mode and the activity ability of the patient, and generating an exercise track through a path planning algorithm, on the basis of micro-current collaborative feedback data, the pain change trend of the patient is calculated, and a motion adjustment plan is generated, a biomechanical model can truly reflect the force distribution conditions of the patient under different motions, the safety of a motion scheme is remarkably improved, and the potential injury risk is reduced; scientific basis is provided for dynamically adjusting the exercise intensity, the obtained exercise adjusting plan can respond to the state change of the patient in time, and the intelligent level and the long-term curative effect of rehabilitation management are further enhanced.

The utility model discloses a quick replacement tool for a hammer head of a radial forging machine, which comprises a horizontally arranged fixed shaft, a chuck is clamped at the right end of the fixed shaft, a clamping plate is fixedly connected to the middle of the fixed shaft, and the left end of the fixed shaft sequentially penetrates through the clamping plate and a clamping device and is slidably sleeved in a sleeve fixedly connected to the center of the clamping device. A nut is arranged between the end of the fixing shaft and the sleeve, a transmission shaft in threaded connection with the nut penetrates through the center of the nut, the left end of the transmission shaft is in threaded connection with a handle seat, the handle seat is in threaded connection with a handle, and meanwhile a pressing disc and a pressing ring are sequentially arranged between the transmission shaft and the handle seat so that the transmission shaft and the clamping device can be clamped and fixed. The hammerhead is clamped and fixed through the clamping device and the clamping plate, then the hammerhead is directionally moved to a main machine installation position through the chuck clamping and fixing shaft, the whole process depends on mechanical automatic operation, efficiency is high, safety is high, and personal injury risks caused by manual operation are avoided.

The invention relates to the technical field of sports protection, in particular to an intelligent protection system for lower limb joints in sports. Comprising a mounting bandage, a wearing support plate, a controller, an attitude sensor and a joint angle sensor, a driving device is arranged on the wearable supporting plate; the controller is arranged on the wearable supporting plate and comprises a data calculation module used for calculating and analyzing data of the attitude sensor and the joint angle sensor and recognizing abnormal signs of a wearer; the emergency warning module is used for sending warning information when abnormal signs are recognized; the alarm module is used for carrying out alarm processing when the warning information is sent out and measures cannot be taken within a preset time; the motion recognition module is used for recognizing the motion type of the wearer through the attitude sensor, generating a wearable supporting plate rigidity control signal according to the joint risk of the motion type, and adjusting the rigidity of the wearable supporting plate through the driving device. According to the technical scheme, the injury risk of lower limb joints in movement can be effectively reduced.

A computer-implemented method, system, and computer program product for predicting fatigue and injury risk for users, such as workers performing material handling operations, in real-time. Motions performed by a user, such as lifting, carrying, and manipulating objects, are captured in real-time, such as via camera-based optical devices, electromyography sensors, and inertia measurement units. Upon capturing motions performed by the user, such captured motions are utilized by a trained machine learning model to predict fatigue and injury risk to users. Such a prediction is made by the trained machine learning model by comparing the captured motions to predefined thresholds of range of motion constraints based on the biomechanical parameters of the user from a digital twin model of the user. Feedback may then be provided based on the predicted fatigue and injury risk of the user, such as in the form of video, audio, and / or haptic alerts.

The invention discloses an elderly lower limb rehabilitation training assessment method and system based on enhanced training, and relates to the technical field of intelligent exercise rehabilitation assessment, and the method comprises the following steps: S100, building an individual rhythm observation system, continuously collecting lower limb muscle contraction rhythms and stride rhythms, generating a rhythm data set, and storing the rhythm data set; a complete rhythm fingerprint band is extracted from the rhythm data set to serve as a benchmark reference for frequency regulation and control; and S200, performing micro-amplitude frequency sweeping based on the rhythm fingerprint band, analyzing rhythm data changes in real time, identifying fine vibration peak characteristics in rhythms, calibrating a phase overlapping region, and generating a rhythm segment. According to the method, through the steps of rhythm fingerprint construction, risk rhythm recognition, buffering and energy dissipation introduction and the like, dynamic regulation and control of the lower limb rehabilitation training rhythm of the old people are achieved, high-frequency resonance is avoided, the injury risk is reduced, and the training safety and the individual rhythm adaptability are improved.