53 results about "Abnormal movements" patented technology

The invention discloses an intelligent ankle rehabilitation exercise management system based on multi-modal data fusion, which belongs to the field of medical assistance and comprises an acquisition processing module, a feature compensation module, an action extraction module, a structure modeling module, a judgment and detection module, an error correction guide module, a management scheduling module, a decision execution module and a report generation module. The motion state of each patient can be comprehensively reflected, fine-grained detection of abnormal actions is achieved, the patient is guided to correct the actions through multi-modal feedback, the secondary injury risk caused by wrong action accumulation is reduced, and the training effect and the controllability of rehabilitation progress are improved; the training plan can be continuously adjusted along with the rehabilitation progress of the patient, dynamic evolution is achieved, meanwhile, insufficient training or potential risks can be rapidly found, doctors and the patient are helped to clearly know the rehabilitation progress, the rehabilitation scheme is continuously optimized, and the training process is more efficient.

The embodiment of the invention provides an intelligent alarm method and device for tumble monitoring, and the method achieves the effective protection of the privacy of a user through the innovative design of a low-position visual angle collection mechanism, the setting of the installation height of a camera not exceeding the knee joint, and the starting of collection only during the abnormal motion. A detection mechanism based on a human skeleton model is constructed, and an accurate fall judgment strategy is established by combining multi-part vertical distance analysis and motion state parameters. An intelligent alarm mechanism is introduced, and quick response of an emergency contact person is realized through key video packaging and emergency channel establishment. According to the method, while the privacy of the user is protected, the defects of the traditional technology in the aspects of data acquisition, tumble detection, alarm response and the like are effectively overcome, and the practicability and reliability of tumble monitoring are remarkably improved.

The invention discloses a dynamic risk early warning method and system based on student exercise data. The method comprises the steps that S1, original exercise data of student exercise is collected through wearable sensing equipment; s2, obtaining mapping motion data through an improved near-end depth Q network based on the original motion data and the normal motion data; s3, according to the mapping relation data, obtaining an action risk point through threshold comparison and considering duration time; and S4, performing matching detection on the obtained action risk points through an abnormal action chain type detection mechanism based on motion time-space association to obtain a risk prediction result, thereby avoiding misjudgment of a single tiny deviation as a risk, capturing a damage trend of a continuous abnormal action, scientifically fitting a motion damage occurrence rule, and accurately adapting to various campus motion scenes.

1. The name of the design product: distribution pole tower positioning monitoring terminal. 2. The use of the design product: for the remote real-time monitoring and management of the position reporting and tilt, abnormal movement and other states of the distribution poles in the distribution network. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention discloses a reservoir operation safety supervision system and method, and relates to the technical field of reservoir safety supervision, the system comprises a biological information monitoring module, a fishing information monitoring module and an ecological system regulation and control module, foreign organisms in a reservoir are monitored so as to prevent the foreign organisms from damaging the ecological system of the reservoir, and the safety of the reservoir is ensured. Meanwhile, the types and the number of original organisms in the reservoir are monitored, so that targeted number regulation and control are performed, the balance and the stability of a reservoir ecological system are guaranteed, meanwhile, the fishing information of each fisherman is monitored in a fishing area, the abnormal movement and the abnormal behavior of each fisherman and the safety condition of used bait are analyzed, and the safety of the fisherman is improved. And alarm prompts can be given to all the fishermen and all the workers in time, so that the damage of illegal behaviors of the fishermen to the ecological system of the reservoir is avoided, and the safety of all the fishermen is also ensured.

A system is provided.SOLUTION: A system comprising: means for collecting performance data of an athlete; means for performing a biomechanical analysis to identify abnormal movements and loads; means for suggesting a personalized training program based on the analysis; and means for providing real-time advice and care to the athlete.SELECTED DRAWING: Figure 1

This invention relates to the field of polishing machines, specifically disclosing a concrete polishing machine with protective functions. It includes a base with a hand handle, a mounting plate on the base, a polishing assembly on the base, a drive assembly connected to the polishing assembly on the base, a push assembly on the base, a brake assembly between the push assembly and the polishing assembly, and an inflation assembly on the mounting plate. This invention provides primary buffering, secondary buffering, braking, and support functions. When the hand is released, it effectively counteracts the polishing machine's continuous spinning and irregular movements, providing effective protection against abnormal movements from multiple angles. This ensures the polishing machine quickly stops working after an malfunction, reducing collision damage and preventing malfunctions, minimizing threats to surrounding workers, improving work efficiency, and accelerating construction progress.

The invention belongs to the technical field of object state monitoring and alarming, and relates to an abnormal action monitoring method for a flame phagocytic experiment of protective equipment. The method comprises the following steps: extracting a protective equipment target contour surrounded by flame in each frame of image in a video stream, and segmenting the protective equipment target contour into a plurality of key body areas; constructing a flame pixel probability map for each key body area in a YUV color space, obtaining anti-interference local box dimensions of the key body areas, and forming fractal feature vectors by the anti-interference local box dimensions of all the key body areas; extracting a time sequence value of each dimensional component in the fractal feature vector in a preset time sequence window to obtain a global descriptor; and calculating a weighted geodesic distance between the global descriptor and a pre-constructed normal action state, and when the weighted geodesic distance is continuously greater than a preset attitude instability threshold in a set continuous frame, judging that an abnormal action is monitored and triggering an alarm. According to the invention, abnormal actions of the dummy or the subject in the experiment can be accurately and reliably monitored.

The invention provides a method for judging an abnormal action in repeated mechanical actions. The method comprises the following steps: acquiring a first continuous frame image corresponding to at least one action period when equipment normally runs; converting the first continuous frame image into a first feature vector; training a feature prediction model through the plurality of first input variables and the plurality of first output variables; acquiring a second continuous frame image corresponding to the mechanical action of the equipment during operation; converting the second continuous frame image into a second feature vector; inputting a second input variable into the trained feature prediction model to obtain a prediction feature vector; calculating the similarity between the predicted feature vector and the actually measured feature vector; and after the plurality of similarities are obtained, calculating frequency domain characteristics of the plurality of similarities, judging whether the frequency domain characteristics meet preset conditions or not, and if not, judging that the equipment has an abnormal action. According to the method, whether the equipment has the abnormal action or not can be timely and accurately judged through the frequency domain characteristics of the mechanical action.

This invention relates to the field of rehabilitation assessment technology, specifically to an upper limb rehabilitation assessment platform and method based on computer vision and the MYO armband. In this invention, an isolated forest is used to perform sparse scoring on the periodic feature vector composed of the mean angle, electromyographic delay, and trajectory fluctuation difference to identify abnormal segments, improving the accuracy of locating repetitive abnormal movements. The graph structure integrates the electromyographic activation difference and mutation trend between nodes, and uses a graph neural network to process the connection sequence of jumping nodes and inter-frame structural changes to obtain the trajectory of multi-node state changes, enhancing the structural expression ability of temporal mutations and electromyographic activation linkage. The number of angle reversals, peak acceleration, and electromyographic fluctuation amplitude are extracted segmentally and then concatenated to form a fusion feature table. The normalized feature values ​​are mapped to level labels using an interval matching method, establishing a traceable feature index path between the segmented behavioral structure and the level output, improving the stage-specific recognizability of the rehabilitation state and the granularity of the output expression.

The application belongs to the technical field of wearable exoskeleton devices, and provides a wearable exoskeleton device suitable for rehabilitation training of infants with congenital cerebral palsy and an exoskeleton control method. The device comprises a wearable exoskeleton device main body, hip joint drives symmetrically arranged on the left and right sides, knee joint drives, ankle joint drives, a controller and multiple groups of sensors. The controller stores joint target trajectory data corresponding to multiple training modes, retrieves corresponding trajectory data based on the selected training mode and generates control instructions to actively drive the affected lower limbs to move. The multiple groups of sensors comprise electromyography sensors, inertial measurement units and pressure sensors. The controller identifies abnormal movement modes such as combined responses and extensor spasticity based on sensor signals and dynamically reduces the drive control gain to release abnormal stress in a compliant manner. The application realizes multi-mode active guidance training and asymmetric assistance for infants with congenital cerebral palsy, and has the ability of abnormal mode identification and safe compliant response.

The application discloses a muscle tension rating and abnormal muscle dynamic positioning system and method, and the muscle tension rating and abnormal muscle dynamic positioning system comprises an information acquisition module, an information processing module and a man-machine interaction module, the information acquisition module is used for collecting personal information and surface electromyogram signals of a detected person, the information processing module is used for muscle tension rating, determination of abnormal movement types and positioning of abnormal muscles, the man-machine interaction module is used for information management, evaluation result display, supervision and tracking and input of diagnosis information, and the like. The application can automatically identify muscle coupling abnormalities of a patient in various movement states, analyze internal muscle factors causing movement symptoms of the patient, evaluate a muscle tension disorder level of the patient and assist in positioning of abnormal muscles.

The system provided by the embodiment of the invention aims to improve the safety and convenience of the daily life of the old people. A system according to an embodiment includes a recognition unit, an analysis unit, a provision unit, a detection unit, and a notification unit. The recognition unit analyzes the movement of the pupil and recognizes an object at which the elderly is watching. The analysis unit analyzes the information recognized by the recognition unit and generates voice guidance. The provision unit provides the voice guidance generated by the analysis unit. The detection unit detects an abnormal motion or state of the elderly using an IoT sensor technology. The notification unit notifies a designated contact or medical institution of the abnormality detected by the detection unit.

The application relates to the field of guitar teaching, and specifically discloses a guitar intelligent teaching method, a system and a storage medium, the method comprising the following steps: acquiring input guitar playing data; performing feature extraction through a preset voice algorithm to acquire audio feature information; comparing the audio feature information with preset standard audio features to acquire a difference time interval set; extracting image sequence frames from video data based on the difference time interval set; performing feature extraction on the image sequence frames through a preset hand motion recognition algorithm to acquire a hand motion feature information set, comparing the hand motion feature information set with preset standard hand motion feature information to acquire an abnormal motion information set, and finally outputting corresponding data analysis information according to the current playing. The application detects and recognizes the guitar playing sound and hand motion from two dimensions of audio and video through the guitar playing data input by the user, finds out the problems in playing, and helps the user to adjust and improve in a targeted manner.

The invention discloses an IMU and abnormal posture recognition-based ankle joint sprain prevention device, which comprises a flexible wearing structure worn at the ankle joint of a human body, and is characterized in that a plurality of IMU posture sensing modules are integrated on the flexible wearing structure to collect the posture angle, angular velocity and acceleration data at the ankle in real time; the central control unit is electrically connected with the plurality of IMU attitude sensing modules and is used for judging abnormal actions and outputting control signals; the dynamic limiting execution mechanism is electrically connected with the central control unit and is used for receiving the abnormal action signal and executing dynamic intervention; and the power supply and communication module is arranged on the flexible wearable structure to supply power to the device, and performs wireless data communication with an external mobile terminal for a user to check and train the model. According to the control method of the ankle joint sprain prevention device based on the IMU and abnormal posture recognition, the posture of the ankle joint can be monitored in real time, high-risk twisting actions can be recognized, and a protection mechanism can be started in real time, so that ankle joint sprain is effectively prevented, and the exercise safety is improved.

The present application belongs to the technical field of object state monitoring and alarm, and relates to an abnormal motion monitoring method for flame engulfment experiment of protective equipment. The method comprises the following steps: extracting the target contour of the protective equipment surrounded by the flame in each frame of the video stream, and segmenting the target contour into multiple key body regions; constructing a flame pixel probability atlas for each key body region in the YUV color space, and obtaining the anti-interference local box dimension of the key body region based on the flame pixel probability atlas; constructing a fractal feature vector by using the anti-interference local box dimensions of all the key body regions; extracting the time sequence value of each dimension component in the fractal feature vector within a preset time sequence window to obtain a global descriptor; calculating the weighted geodesic distance between the global descriptor and a pre-constructed normal motion state, and determining that an abnormal motion is monitored and an alarm is triggered when the weighted geodesic distance continuously exceeds a preset posture instability threshold within a set number of continuous frames. The present application can accurately and reliably monitor the abnormal motion of a dummy or a subject in an experiment.

The utility model belongs to the technical field of sports equipment and rehabilitation medical treatment, and particularly relates to a patella belt which comprises a main body and a bandage, the bandage can bind and fix the main body at the position of patella, the main body comprises a woven surface layer, a supporting layer and a bottom surface layer, the woven surface layer is provided with a cutting edge which is complete in the circumferential direction, and the bottom surface layer is provided with a bottom surface layer. The woven surface layer and the bottom surface layer are compounded into a whole by arranging an edge locking structure on the periphery of the woven surface layer and the bottom surface layer, and the supporting layer is clamped and fixed between the woven surface layer and the bottom surface layer. According to the patella belt disclosed by the invention, the patella belt main body structure formed by compounding the woven surface layer, the supporting layer and the bottom surface layer is adopted, so that the patella belt can more accurately adapt to knee joint shapes of different users while keeping high elasticity and stable supporting, and the problem of non-uniform supporting or overlarge local pressure caused by a fixed shape is avoided; when a patient performs knee joint rehabilitation training, abnormal movement of the patella in the movement process is reduced, and the rehabilitation training effect is improved.

The invention belongs to the technical field of health monitoring, and particularly relates to a senile dementia risk home monitoring method based on multi-modal behavior analysis, which comprises the following specific steps: monitoring human body postures in real time, analyzing repeated actions, synchronously early warning abnormal movement, and carrying out voice interaction operation through a built-in AI dialogue terminal; performing behavior, emotion and physiology correlation modeling, executing cognitive decline dynamic evaluation through cognitive function self-test, and judging a risk level according to an analysis and evaluation result; executing a multi-level early warning mechanism based on the risk level information, and automatically taking corresponding emergency measures; the threshold value is dynamically calibrated by integrating user habit data, a closed-loop feedback mechanism is designed to realize false alarm suppression, and a monthly health report is synchronously generated to assist a doctor in adjusting an intervention scheme. The problems that in a traditional monitoring mode, tiny behavior changes are difficult to capture, rescue is delayed due to subjective evaluation hysteresis and passive response, the screening frequency is low, and the scene is limited can be solved.

The invention discloses a rehabilitation exercise posture monitoring and evaluating method and system, and relates to the technical field of exercise monitoring, and the method comprises the steps: obtaining motion feature data, including joint motion data, muscle motion data and spatial position data, corresponding to a target object in real time through a sensor module worn by the target object; performing space-time alignment on the joint movement data, the muscle movement data and the spatial position data; matching a corresponding action judgment rule according to the current rehabilitation stage of the target object; and calculating and outputting an abnormal action probability and an abnormal action type according to the joint movement data, the muscle movement data and the spatial position data based on the action judgment rule, and correspondingly generating movement posture evaluation data. The method can solve the problem that in the prior art, due to the fact that monitoring means are limited in the rehabilitation exercise process, scientific proceeding of rehabilitation exercise cannot be promoted.

The invention provides an intensive care video monitoring alarm reasoning method and device. A server side of the method comprises the following steps: receiving an intensive care index of a patient; wherein the intensive care indexes comprise medical equipment data, adverse event model identification information, micro-action identification information, micro-expressions and limb transactions; and when the collected index is the medical equipment data, identifying and analyzing the data, and sending the analyzed data to a client. By means of the method and device, hospital infection is reduced, and meanwhile all-weather abnormal behavior and sign real-time monitoring and warning services are provided for the patient; through the mode, comprehensive nursing and accurate treatment of the patient are finally realized, the occurrence rate of adverse events is reduced, and the quality and efficiency of medical services are improved.

The present application relates to the technical field of robots, and relates to a self-adaptive path decision optimization method and system of an intelligent logistics robot, comprising: acquiring a plurality of initial images of the intelligent logistics robot, wherein the initial images comprise images of different angles at different times; determining movement information of the intelligent logistics robot by using the plurality of initial images; determining a non-instructional movement range of an abnormal movement structure of the intelligent logistics robot by using the movement information, and determining a target path; adjusting an operation process of the intelligent logistics robot by using the target path, and executing the operation of the intelligent logistics robot by using the adjusted path. According to the present application, a three-dimensional model is established by multi-angle image analysis, and the movement characteristics of a mechanical arm connecting rod are identified, so that the instructional movement and the non-instructional shaking can be effectively distinguished, the compound movement state of the robot base movement and the mechanical arm movement can be accurately distinguished, and the influence of the non-instructional mechanical arm shaking on path planning can be effectively inhibited.

The invention discloses a real-time multi-mode spine dynamic evaluation system and method based on a dynamic DR, and relates to the technical field of spine evaluation, and the method comprises the steps: continuously collecting a two-dimensional dynamic image sequence of a spine in motion through the dynamic DR, and synchronously obtaining body surface pressure distribution data and joint motion track data; inputting the two-dimensional dynamic image sequence into a pre-trained neural differential rendering model, and generating a dynamic three-dimensional model in combination with centrum motion boundary conditions in the joint motion track data; fusing the relative displacement of the vertebral body, the curvature change rate of the spine and the stress concentration coefficient of the intervertebral disc to generate a biomechanical evaluation report, and marking an abnormal movement segment and a degeneration risk level. According to the method, the dynamic DR image sequence and the joint movement track data are fused through the neural differential rendering model, the dynamic three-dimensional model is generated in combination with the vertebral body movement boundary condition, and submillimeter-level biomechanical reconstruction is achieved.

This invention discloses a device and computer program product for automatically detecting the health status of aquatic animals based on spatiotemporal information. The invention constructs the STAFDD spatiotemporal aquatic animal health status detection framework, specifically based on an improved YOLOv8 detector integrating an SPD-CBS module, a coordinate attention mechanism, and a small target detection head to detect images or videos. It employs the ByteSort algorithm with integrated ReID features to achieve stable multi-target tracking, ensuring coherent trajectory extraction. Simultaneously, it introduces an LSTM-based behavior analysis module to effectively identify abnormal movement patterns in diseased fish, and performs joint diagnosis by combining appearance features and behavioral cues. This invention considers both spatial features and temporal information, contributing to more accurate and robust identification of aquatic animal health status.

The utility model provides a sinker of a computerized flat knitting machine, which comprises a sinker and a spring piece, and the spring piece for improving damping generated when the sinker moves is arranged on the sinker. The device has the beneficial effects that through normal pressure of the spring piece on the sinker mounting groove, damping friction force generated when the sinker moves is increased, abnormal movement of the sinker is avoided, the stability of braided fabric is guaranteed, and therefore the quality of the fabric is improved.

The invention relates to a humanoid robot falling failure diagnosis method and system, and relates to the field of robot fault diagnosis, and the method comprises the steps: determining a current abnormal motion combination in response to a robot falling signal; searching an abnormal combination number based on the current abnormal combination; accumulating abnormal times; when the abnormal frequency exceeds a reliable abnormal frequency threshold value, determining a suspected abnormal state of the power panel; executing a PCBA test method based on the suspected abnormal state of the power panel, and outputting a verification completion signal; when the verification completion signal does not exist, a current robot action combination is collected in real time; determining a predicted abnormal action combination based on the current robot action combination and the abnormal combination number; determining predicted risk time; when the predicted risk time is smaller than the shortest processing time threshold value, robot action adjusting parameters are determined; and executing a robot action adjusting operation. The method has high diagnosis accuracy and efficiency, and unnecessary diagnosis can be avoided.

Provided is an integrated monitoring apparatus including: a communication module; and a processor functionally connected to the communication module, wherein the processor acquires connection information related to base station devices to which user terminals in a monitoring space are connected among a plurality of base station devices in the monitoring space through the communication module, identifies whether an abnormal movement flow occurs, based on a change in the connection information, and upon detecting an occurrence of the abnormal movement flow, verifies the abnormal movement flow based on a location of a target terminal related to the abnormal movement flow among the user terminals.

An information processing device according to the present disclosure comprises: a flow line information reception unit that receives flow line information indicating flow lines of a mobile body that are generated on the basis of movement position information indicating movement positions of the mobile body in a region of interest; and a correction unit that, if a flow line in the flow line information includes an abnormal movement range indicating an abnormality in the movement of the mobile body, corrects an abnormal flow line indicating the flow line of the abnormal movement of the mobile body included in the abnormal movement range.