8 results about "Abnormal movements" patented technology

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.

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 provides a method and device for optimizing the motion trajectory of a robot, equipment, a medium and a product, and relates to the technical field of artificial intelligence. The application realizes fine evaluation of the quality of the motion trajectory, and accurate positioning of the abnormal range and abnormal motion state. The target function is used to locally optimize the abnormal motion state of the abnormal range, ensuring that the optimized motion trajectory meets the standard intention of expert behavior and guarantees the task target, while improving the fluency and environmental safety of the motion trajectory. The application realizes a closed-loop optimization mechanism from quality quantization to abnormal positioning to smooth repair of the motion trajectory, which can effectively reduce the abnormal trajectory segments in the existing generated motion trajectory of the robot. The final output of the optimized motion trajectory conforms to the high-quality data required by the robot imitation learning training field, providing reliable technical support for breaking through the bottleneck of insufficient training data in the imitation learning field.

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.

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.

A multidimensional abnormal motion detection system (100) for detecting abnormal movements of a driven tool, comprising: a three-axis accelerometer sensor (104a); a three-axis gyroscope sensor (104b); a three-axis magnetometer sensor (104c); and a processor (114) with a machine learning model (112) that is pre-trained for the driven tool to determine the occurrence of an abnormal movement event of the driven tool, wherein the machine learning model uses time series data received from the accelerometer (104a), the gyroscope (104b) and the magnetometer (104c) to predict the occurrence of the abnormal movement event, wherein the processor activates a reactive device of the driven tool upon prediction of the abnormal movement event.

The application discloses a security inspection method and system based on a holder and binocular distance measurement, and the method comprises the following steps: collecting the three-dimensional data of each target monitoring personnel in a target area in real time by a binocular distance measurement holder robot, and determining the human body action detail features according to the three-dimensional data; determining the abnormal action existing in the target area according to the human body action detail features, and determining the abnormal action corresponding to the abnormal personnel; comparing the abnormal action with a preset abnormal motive database to determine the potential motive of the abnormal personnel, generating alarm information according to the position information of the abnormal personnel and the potential motive of the abnormal personnel, and moving the binocular distance measurement holder robot to the position where the abnormal personnel is located. Through the implementation of the application, the problem that the behavior and potential intention of the abnormal personnel cannot be accurately identified when the robot inspects in the robot security technology field can be solved, and the security reliability and safety of the robot are improved.