861 results about "Motion detection" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a data processing method and system for micro-motion detection array station distribution, and relates to the technical field of micro-motion detection, and the method comprises the steps: according to the actually measured starting point coordinates and the azimuth angle of a measuring line, setting the measuring point distance, and the radius and number of nested triangle circumcircles, generating field station coordinates in batches; collecting and preprocessing station waveform data; calculating a spatial autocorrelation coefficient of any center station and a non-center station; dBSCAN clustering is carried out based on the station distance; performing spatial averaging on the spatial autocorrelation coefficient according to a clustering result; fitting the averaged spatial autocorrelation coefficient and a zero-order Bessel function to obtain a frequency dispersion spectrum, picking up a surface wave phase velocity in the frequency dispersion spectrum, and calculating an apparent shear wave velocity; inverting the shear wave velocity of the waveform data by adopting a self-adaptive gradient inversion strategy; and generating a velocity comprehensive profile of a depth domain and a frequency domain according to the surface wave phase velocity, the apparent shear wave velocity and the inverted shear wave velocity. The invention provides a new method for station distribution to data processing for micro-motion detection.

The invention discloses an intelligent monitoring system and method for nursing infants, and the method comprises the following steps: S1, collecting continuous frames of infrared images, carrying out the preprocessing, and extracting feature vectors; s2, constructing a classification model for identifying infant micro-motion types, and training the classification model to ensure accurate identification of the classification model; s3, acquiring a real-time infrared monitoring video image of the infant, performing similarity analysis on continuous frames, judging the range of the continuous frames of the micro-action, and extracting continuous frame images; and S4, feature vectors are extracted from the extracted continuous frame images, the type of the infant micro-motion is identified, and user side reminding and analysis are carried out. Aiming at low-light environment and micro-motion detection in baby nursing, through combination of infrared imaging and optical flow feature extraction and fusion of a perceptual hash array and a Hamming distance, real-time and reliable baby state monitoring service is provided for parents or caregivers, and the safety and convenience of baby nursing are enhanced.

The invention relates to the technical field of motion detection, in particular to an old people falling early warning system based on motion trail analysis, which comprises a gravity center identification module, a trend identification module, an inertia pushing module, a phase judgment module and a grade response module. According to the method, the vertical coordinates of the key points of the waist and the two feet in the image frame are acquired, and the height difference change is continuously tracked, so that the potential falling initial state can be identified from the static posture, the vector included angles among the three groups of bone points of the hip knee, the knee ankle and the shoulder pelvis are subjected to sequence analysis, and whether synchronous disintegration occurs in the coordination action is identified; the identified trend sequence is mapped according to the risk level, a differential response mechanism is applied, a high-density corresponding relation is constructed between the behavior trend and the alarm level, graded response and time sequence triggering of the falling risk are achieved, the accurate identification capability of the falling precursor action is enhanced, the sensitivity and discrimination of the alarm response are also improved, and the safety of the falling risk is improved. And misjudgment and missed judgment are effectively avoided.

The invention relates to the technical field of liquid crystal display driving control, and discloses a high-refresh-rate liquid crystal display driving method and system, and the method comprises the steps: a motion amount detection module calculates the motion intensity of a dynamic region through inter-frame comparison; the voltage excitation adjusting module applies voltage according to dynamic / static region differentiation, and the dynamic region adopts asymmetric voltage to increase the response speed; the gray scale adjustment module dynamically optimizes the voltage amplitude based on the local contrast so as to smooth the gray scale; the self-adaptive refresh rate module fuses the motion intensity and the image type to switch a refresh rate gear; and the charge balance management module intelligently triggers regional reverse voltage pulses in a static scene to eliminate ghosting. The system eliminates ghost shadows through multi-module cooperation and dynamic charge balance management, intelligently switches refresh rate gears based on motion intensity and cooperates with voltage regulation to reduce static power consumption, adopts regional gray scale compensation to optimize detail display, and combines asymmetric high-voltage driving to improve the response speed of liquid crystal molecules.

The invention discloses a PDMS / MWCNTs flexible resistive pressure sensor with a double-layer corrugated structure and a preparation method of the PDMS / MWCNTs flexible resistive pressure sensor, and belongs to the technical field of flexible piezoresistive sensors. The PDMS / MWCNTs flexible resistive pressure sensor with the double-layer corrugated structure sequentially comprises a silica gel packaging layer, a copper foil, a PDMS / MWCNTs conductive thin film with a high-amplitude corrugated structure, a PDMS / MWCNTs conductive thin film with a low-amplitude corrugated structure, a copper foil and a silica gel packaging layer from top to bottom, the cross section of the PDMS / MWCNTs conductive thin film with the high-amplitude corrugated structure and the cross section of the PDMS / MWCNTs conductive thin film with the low-amplitude corrugated structure are both in a wave shape, and the wave length of the wave shape of the PDMS / MWCNTs conductive thin film with the high-amplitude corrugated structure is the same as that of the wave shape of the PDMS / MWCNTs conductive thin film with the low-amplitude corrugated structure. And the wave height of the wave shape of the PDMS / MWCNTs conductive thin film with the high-amplitude corrugated structure is higher than that of the wave shape of the PDMS / MWCNTs conductive thin film with the low-amplitude corrugated structure. The flexible resistance type pressure sensor realizes high linearity and good sensitivity in a wide pressure range, has excellent mechanical stability, rapid responsiveness and good durability, and has great potential in the fields of human wearable health monitoring, motion detection and the like.

The invention provides a mechanical arm motion precision detection system and method based on moire fringes, and relates to the field of image data processing, and the system comprises a grating projection module which is used for determining grating parameters based on motion information of a mechanical arm and projecting a grating pattern to a detection surface of the mechanical arm based on the grating parameters, the grating parameters at least comprise grating density, grating angle and grating type; the image acquisition module is used for acquiring continuous frames of moire fringe images in the movement process of the mechanical arm; and the data processing module is used for carrying out phase calculation on the continuous frames of moire fringe images and resolving the displacement error, the rotation angle error and the vibration error of the mechanical arm, and the motion detection precision of the mechanical arm is improved.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

Motion detectors can include a housing defining a first cavity and an aperture extending through the housing. A circuit board can be disposed in the first cavity. An infrared sensor and a light sensor can be mounted on the circuit board. A lens can extend across the aperture. A wall can extend between the lens and the circuit board such that the wall, the lens, and the circuit board define a second cavity at least partially within the first cavity and the second cavity contains the infrared sensor and the light sensor.

The invention relates to an imaging type non-contact gating signal obtaining method for a magnetic resonance system, and belongs to the field of physiological signal detection.The method comprises the steps that pulse and respiration signals of a magnetic resonance detection patient are collected through a camera, and face information of the patient is obtained through a reflector in a magnetic resonance instrument cavity; a living body skin detection technology is utilized to efficiently select a region of interest of the face of a patient, and an IPPG technology is combined to process a video image to obtain a curve of image gray along a time sequence; a motion detection algorithm is used for tracking micro motion of the chest, and stable and accurate breathing signals are extracted by combining frequency selection. By analyzing and quantifying pulse wave signals and delay of respiration signals and electrocardiosignals, the purpose of replacing electrocardiosignal R-peak gating triggering with pulse wave and respiration signal feature triggering is achieved. The invention provides an imaging type non-contact gating technology to solve the problems that traditional ECG and other contact gating technologies are easily affected by a strong static magnetic field, an alternating gradient magnetic field and a radio-frequency pulse electromagnetic field in a magnetic resonance imaging system, and motion artifacts can be generated on motion of fluid such as human blood, breathing and cardiac contraction. Therefore, the quality of a magnetic resonance imaging image is improved.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

An approach is provided for computer-vision-based object motion detection. The approach involves receiving a video sequence captured from a perspective of a vehicle / device traveling at street level. The approach also involves processing frames of the videos sequence using computer vision to determine semantic localization feature(s) associated with object(s) detected in the frames. The approach further involves clustering the object(s) into object cluster(s) across the frames based on the semantic localization feature(s). The approach further involves creating an object vector connecting the object(s) in a given cluster of the object cluster(s) across the frames. The approach further involves computing an angle between the object vector and a reference vector associated with a movement of the vehicle / device across the frames. The approach further involves classifying a motion of the object(s) relative to the vehicle / device based on the angle.

The invention discloses a screen structure, an intelligent ground screen, a monitoring method, a medium and a computer product. The stress layer is suitable for being in contact with a target object, the circuit board layer is arranged below the stress layer, the force conducting layer is arranged below the circuit board layer, the force sensing layer is arranged below the force conducting layer, the force sensing layer comprises a pressure sensor, the force sensing layer abuts against the force conducting layer so as to be suitable for monitoring pressure from the force conducting layer, and the force sensing layer is connected with the base. Wherein the stress layer comprises a mask structure and a force conduction structure, the force conduction structure is provided with two opposite ends in the direction from the base to the stress layer, the first end is connected with the mask structure, the second end is connected with the force conduction layer so that the pressure of the mask structure can be conducted to the force conduction layer, the circuit board layer is provided with a through hole, and the force conduction structure penetrates through the through hole. According to the invention, the motion state of the target object can be accurately monitored in real time, and a high-sensitivity and high-precision circuit system can be protected from being damaged in frequent high-load motion detection.

A smart doorbell, for a security system, which is capable of performing functions including, person detection, facial recognition, motion detection, detection of baby or pet running away, weapon detection, fire detection, detection of parcel left at porch, parcel theft, dog poop detection, vehicle detection, animal detection, eavesdropper detection, a person jumping from backyard wall, and intruder detection. The functions are detected through a camera of the doorbell and, subsequently, AI-based alerts are generated.

The invention discloses a video restoration data set construction and restoration method combined with generation of a large model, and belongs to the technical field of video restoration, and the method comprises the following steps: S1, slicing; s2, screening: performing aesthetic scoring and motion detection on the slices, and screening and retaining high-dynamic and high-image-quality fragments; s3, segmenting a core object; s4, extracting text features; s5, model input; s6, performing feature fusion; s7, model training: only training parameters of the cross attention layer, and locking gradients of other layers to reduce the calculation cost; s8, calculating a loss function; s9, video input: inputting a to-be-restored video, and generating an object Mask and text description of the to-be-restored video; and S10, outputting the restored video: inputting the Mask video, the object Mask and the text description into the improved generated large model, and outputting the restored video. According to the staged feature injection strategy, the global naturalness and the local reality sense are considered, and the visual consistency of the repaired content and the original video is remarkably improved.

The invention discloses a port container cargo loading and unloading state identification method based on computer vision, and the method comprises the steps: collecting visible light and infrared video images in a port, and carrying out the preprocessing of the images through combining with a defogging algorithm; carrying out the recognition and classification of port objects in the video based on an improved OfficientDet target detection model, and dynamically adjusting the feature fusion weight; in combination with a motion detection technology, if a target is detected to move, the target is divided into a truck type; otherwise, classifying into a'container 'class; further optimizing and classifying the detection target based on an ST-MRF image segmentation technology, and extracting a container class; using a DINO-X target detection model to identify whether a container cover exists or not, and marking a container without a cover as'no goods, to be loaded and unloaded '; and through a multi-feature fusion loading and unloading state probability model MFSM, the container cover coverage rate, the cargo height and the inclination angle are integrated to judge the state of the container. The accuracy and efficiency of container loading and unloading state recognition are improved, and the intelligent level of port container loading and unloading management is improved.

The invention discloses a visual intelligent monitoring alarm system for home-based care for the aged, relates to the technical field of intelligent monitoring, and aims to solve the problem that effective safety monitoring and abnormality checking cannot be performed on the living environment of the aged. According to the invention, the early warning intensity can be automatically adjusted, interference of excessive alarm to users is avoided, and important abnormity can be timely and effectively processed. The mechanism for dynamically adjusting the early warning intensity improves the flexibility and the response speed of the system, generates the corresponding alarm type according to the abnormal type of the abnormal monitoring data, and matches the corresponding alarm degree, so that the alarm information is more specific and clear, and through multiple monitoring of face recognition, motion detection, sound analysis and the like, the accuracy of the system is improved. The system can quickly identify abnormal behaviors, trigger an alarm mechanism and ensure the safety of a home-based care environment.

The invention relates to the field of flexible sensing, and discloses a flexible dual-mode sensor with a foot arch and motion detection function, the flexible dual-mode sensor comprises an upper substrate, a lower substrate, an electrode layer, a conductive pin, a PVDF doped BTO-NPs nanofiber film and a PVDF doped ionic salt solution nanofiber film, the upper substrate and the lower substrate are polyimide films (PI), the electrode layer is an immersion gold (Au) pad, and the conductive pin is arranged on the electrode layer. The conductive pin is formed by fixing a copper wire on the electrode plate through silver paste, the nanofiber membrane is obtained by PVDF doped BTO-NPs and PVDF doped ionic salt electrostatic spinning, the gold immersion (Au) bonding pad membrane is designed on the PI substrate to form polar plates and electrodes of the capacitive sensor and the piezoelectric sensor, and the electrode plate is led out through the copper wire to serve as the conductive pin. According to the flexible dual-mode sensor, the flexible PI in a specific shape is prepared to serve as the upper substrate and the lower substrate of the flexible dual-mode sensor, and the gold immersion (Au) bonding pad films are designed on the PI substrates, so that the flexible sensor has flexibility and ductility and is more attached to surfaces with different curvatures.

[Object]Provided is a novel and improved technology capable of distinguishing the treatment of a predetermined object recognized from image data depending on the situation.[Solving Means]Provided is an information processing apparatus including an image recognition unit configured to recognize an object from image data, a motion detection unit configured to detect motion in sensing data acquired by a sensor, and an object detection unit configured to detect an object recognized as a predetermined object by the image recognition unit and corresponding to a region in which motion has been detected by the motion detection unit.

The invention discloses an epilepsy signal identification method and system based on multi-modal information of wearable equipment. The method comprises the following steps: firstly, preprocessing acquired multi-modal physiological signal data; calculating a time-frequency feature through a sub-band analysis coefficient, extracting a time domain feature, a frequency domain feature and a nonlinear feature after reconstruction, and constructing a multi-modal feature matrix; obtaining an action feature matrix through a time-frequency feature threshold classifier; performing T test, correlation analysis and recursive feature elimination, and screening to obtain a final training set and an optimal feature list; respectively training an LSTM attack detection model and a random forest motion detection model, and constructing a serial ensemble learning framework; and finally, outputting a risk value based on a sliding window, triggering an alarm when the risk value continuously exceeds a threshold value, and starting a misinformation suppression mechanism in a non-response period. According to the method, non-attack motion interference is filtered through a designed serial integrated learning framework, the false alarm rate of the system is remarkably reduced, meanwhile, high detection sensitivity and accuracy are kept, and the real-time processing efficiency and practicability of the system are improved.

A communication-enabled power management system is disclosed. The system includes a self-contained receptacle outlet comprising a housing, a power receptacle, a power sensor configured to monitor a plurality of power parameters, a motion detection module, a transceiver, and a processor with a unique identifier. The processor connects the receptacle to a remote controller and allows the user to toggle between power states. The system further includes a predictive analytics module to optimize power usage based on occupancy patterns, and an independent power backup management module for maintaining power during failures. The system also communicates with secondary devices, such as vents and switches, to manage energy distribution and respond to emergency events.

The invention relates to a pressing plate operation behavior analysis and error prevention method and system based on image recognition and machine learning. The method comprises the following steps: acquiring a pressing plate operation area video stream through image acquisition equipment, and segmenting a video into independent operation event segments based on motion detection and trajectory analysis; extracting key point time sequence data of hands of an operator by using a human body posture estimation model, and constructing a dynamic feature sequence fusing a spatial relationship, kinematics and posture semantics; performing multi-level similarity comparison on the dynamic feature sequence and a standard operation template, and judging operation compliance by a machine learning model in combination with a dynamic threshold value; triggering graded early warning and intervention according to a judgment result; and an incremental learning mechanism is adopted, and the template and the threshold value are adaptively optimized based on historical data. According to the invention, accurate and intelligent analysis and active error prevention of the whole operation process of the pressing plate are realized, and the safety level of electric power operation is effectively improved.

The invention relates to the technical field of motion detection alarm, in particular to a multi-agent fusion perception area intrusion abnormity alarm method and system, and the method comprises the following steps: collecting a target coordinate and a timestamp, integrating and sorting tracks, analyzing the direction and time change, judging the stability, recognizing an induction behavior, and screening and verifying the abnormity. According to the method, through integration of the spatial position and time information of the multi-source sensing intelligent agent, the continuous track of the target is reconstructed, the integrity identification of the motion behavior is enhanced, and the track direction change and time interval linkage analysis is carried out, so that the sensing depth of the behavior trend is improved; multi-dimensional recognition of abnormal behaviors is achieved through track stability and regional stay state linkage judgment, continuous conditions are introduced into abnormal verification to filter accidental interference, alarm output is matched with a regional strategy based on a verification result, the accuracy and robustness of intrusion detection are effectively improved, and the practical requirement in a high-dynamic scene is met.

Systems and methods for enrolling and authenticating a user in an authentication system via a user's camera of camera equipped mobile device include capturing and storing enrollment biometric information from at least one first image of the user taken via the camera of the mobile device, capturing authentication biometric information from at least one second image of the user, capturing, during imaging of the at least one second image, path parameters via at least one movement detecting sensor indicating an authentication movement of the mobile device, comparing the authentication biometric information to the stored enrollment biometric information, and comparing the authentication movement of the mobile device to an expected movement of the mobile device to determine whether the authentication movement sufficiently corresponds to the expected movement.

The invention discloses a fracture postoperative lower limb dynamic load rehabilitation training system based on multi-modal fusion, and the system comprises a pressure sensing module which is used for collecting the pressure data of different areas of a sole in real time, and transmitting a pressure signal to an edge calculation control unit; the electromyographic signal acquisition module reflects the working state of the muscle by acquiring an electrical activity signal of the muscle, and sends an electromyographic signal to the edge calculation control unit for calculation of a dynamic safety load model and judgment of the training state by the system; the micro-motion detection module is used for monitoring micro-motion displacement of a fracture part in real time, and the initial parameter input module is connected with the edge calculation control unit and used for collecting and processing data of a fracture area of a patient; according to the invention, the safety and effectiveness of rehabilitation training are improved, the safety of a patient in the rehabilitation training process is guaranteed, the possibility that the patient is subjected to secondary injury is reduced, the quality and effect of rehabilitation training are improved, and the intelligence and visualization degree of the rehabilitation training process is improved.

Embodiments are disclosed for head tracking state detection based on correlated motion of a source device and a headset communicatively coupled to the source device. In an embodiment, a method comprises: obtaining, using one or more processors of a source device, source device motion data from a source device and headset motion data from a headset; determining, using the one or more processors, correlation measures using the source device motion data and the headset motion data; updating, using the one or more processors, a motion tracking state based on the determined correlation measures; and initiating head pose tracking in accordance with the updated motion tracking state.

Data obtained from synthetic aperture radar "SAR" image acquisition apparatus on a satellite, operating in orbit around Earth, is processed to form looks at a target area on Earth at different points in time. Dense optical flow is computed to identify apparent motion between successive looks and to determine apparent velocity for at least some pixels between successive looks. The apparent velocity is converted into actual velocity by correction for the relative motion between the platform and the target. A 2D velocity map of motion in the target area may be generated.

A solid-state lighting fixture assembly having a lighting fixture with a socket configured to receive a plug associated with one or more accessories to allow for easy in-field mounting of accessories, e.g., controls, onto installed lighting fixtures. The socket may be internally electrically connected to an auxiliary power output of a driver and / or to a battery power pack within the lighting fixture assembly, thereby providing direct-current voltage power for the accessory and, also, allowing for signal transmission to and from the accessory. Each accessory includes one or more sensors and communication components to provide the connected lighting fixture assembly with specific capabilities including, but not limited to, motion detection, ambient light level detection, ambient temperature measurement and wireless communications. The wireless communication can also be used to control one or a group of lighting fixtures and transmit sensor data associated with, for example, monitoring space utilization and asset tracking.

Systems and techniques are described for image signal processor motion detection. In various examples, first image data including pixel values may be generated, where each pixel value is associated with a respective color tag of a color filter array. A first set of the pixel values corresponding to a first block may be determined. A second set of pixel values may be determined based on a respective pixel value of the first set and the respective color tag associated with that respective pixel value. A first intensity value associated with the first block may be determined based on the second set of pixel values. A second intensity value associated with a reference block may be determined. A comparison value representing a difference between the first intensity value and the second intensity value may be determined. A signal indicating detected motion may be sent based on the comparison value.

An abnormality detection device includes a motion detection unit that detects a motion indicating pain associated with a disease of a body of a driver based on motion information of the driver acquired by a camera, a behavior detection unit that detects abnormal behavior of a vehicle based on behavior information acquired by a vehicle behavior information acquisition unit, a biometric information detection unit that detects biometric information that is an abnormal sign of the driver based on biometric information acquired by the biometric information acquisition unit, and an abnormal sign determination unit that determines whether the driver shows an abnormal sign in the body by using all or some of a detection result of the motion detection unit, a detection result of the behavior detection unit, and a detection result of the biometric information detection unit.