27 results about "Activity classification" patented technology

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

A system and method for person identification from video data by disentangling biometric identity features from non-biometric appearance and activity features are disclosed. The system processes RGB video sequences depicting individuals performing various activities to extract spatio-temporal features. These features are separated into distinct biometric identity representations and non-biometric features related to appearance and performed activities. To achieve this separation and minimize appearance bias, the system utilizes an auxiliary supervisory model. At least two implementations of this supervisory model are disclosed: one using semantic supervision via structured embeddings processed through a vision-language model, and another employing silhouette-based feature distillation from a silhouette-trained neural network. Joint training for biometric identification and activity classification ensures accurate identification of individuals independently of facial visibility, clothing differences, or activity variations.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

This application discloses a method, apparatus, and electronic device for determining user activity. The method includes: acquiring first feature information reflecting the activity of any user; clustering a first user group according to the first feature information and a preset clustering algorithm to obtain at least one user cluster, wherein each user cluster corresponds to a certain level of activity; determining multiple activity classification criteria according to the at least one user cluster and a preset classification model, wherein each activity classification criterion involves constraints on the first feature information; and determining the activity of a target user according to a target activity classification criterion among the multiple activity classification criteria.

Methods, systems, and devices for activity classification are described. A system may receive physiological data associated with a user via a wearable device, where the physiological data includes at least motion data. The system may identify an activity segment during which the user is engaged in a physical activity based on the motion data, where the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment. The system may generate activity classification data associated with the activity segment based on the activity segment data, the activity classification data including a set of classified activity types and corresponding confidence values. The system may then cause a graphical user interface (GUI) of a user device to display the activity segment data and at least one classified activity type of the set of classified activity types.

One or more computing devices, systems, and / or methods are provided. In an example, a method comprises receiving, by a device, incoming motion data from a motion sensor, generating, by the device, an incoming embedding vector based on the incoming motion data, generating, by the device, a predicted embedding vector based on the incoming embedding vector, assigning, by the device, an activity classification based on the predicted embedding vector, and modifying an operating parameter of the device based on the activity classification.

The embodiment of the invention provides a visual and auditory processing and brain activity classification method and system based on EEG space-time-frequency characteristics. The method comprises the steps that multi-band electroencephalogram data are obtained; wherein the multi-frequency-band electroencephalogram data represents brain activity data collected under different frequency bands; performing visual and auditory preprocessing on the multi-band electroencephalogram data to obtain a preprocessed multi-band electroencephalogram signal; performing clustering analysis on the basis of the multi-frequency-band electroencephalogram signals to obtain micro-states of various stimuli under different frequency bands; calculating a micro-state attribute of the micro-state, and performing relevance calculation based on the micro-state attribute and a predetermined multi-band brain network to obtain a space-time frequency relevance vector; and through a predetermined visual and auditory processing brain activity identification model, carrying out brain activity identification on the space-time-frequency association vector to obtain a brain activity classification result. According to the scheme, the brain activity classification accuracy can be improved.

An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

Systems and methods for scraping detection include a device which receives a plurality of requests from a client to extract data from a resource. The device may classify activity of the client as activity of an autonomous program based at least on a number of the plurality of requests, and one of i) one or more content types of the requests, or ii) a frequency in which the requests are received. The device may block, responsive to classification of the activity, a subsequent request from the client to extract data from the resource.

The application provides a portable full-automatic fluorescence spectrum sperm activity detection method and system, and belongs to the technical field of sperm activity detection. The method comprises the following steps: placing a semen sample to be detected into a detection kit to which a sperm activity fluorescence biological dyeing agent is added; the sperm activity fluorescence biological dyeing agent comprises a CM-Dil biological derivative activation dyeing solution; fluorescence spectrum information of the semen sample to be detected is acquired through a far-field optical fluorescence microscope and a microscopic optical information acquisition assembly; the fluorescence spectrum information is subjected to data analysis through a spectrum analysis system, so as to obtain survival rate information, development state information and activity classification information of sperm in the semen sample. The application realizes that a person to be detected can complete sperm activity detection by himself without going to a hospital; and the detection result contains the survival rate information, the development state information and the activity classification information of sperm in the semen sample, so that whether the person to be detected has the ability to reproduce can be accurately determined.

The application relates to the technical fields of computer vision, sensor signal processing and artificial intelligence, and specifically provides a low-power millimeter wave radar human behavior recognition method and system, which comprises the following steps: preprocessing point cloud data collected by a millimeter wave radar to obtain a structured feature sequence; extracting multi-level manual features from the structured feature sequence, and performing feature selection and normalization to generate an optimized feature vector; inputting the optimized feature vector into a heterogeneous integrated classifier, combining a dynamic pseudo-label strategy to perform semi-supervised training on the heterogeneous integrated classifier, and obtaining an enhanced recognition model; and using the enhanced recognition model to perform human activity classification and recognition on millimeter wave radar data to be recognized. The application significantly improves the recognition accuracy and robustness of behavior type recognition from millimeter wave radar data.

Techniques and apparatus are described for performing fusion of audio plethysmography and motion sensing data. Fusing motion sensing data with audio plethysmography expands the situations in which audio plethysmography can operate. In one aspect, fusion of audio plethysmography and motion sensing data (202) can be used for motion artifact filtering (206). Motion artifact filtering (206) can be used to attenuate noise caused by the movement of the user (106), improving the sensitivity and accuracy of audio plethysmography. This improved performance expands the ability of audio plethysmography to support use cases during situations in which the user (106) is engaged in activity. In another aspect, fusion of audio plethysmography and motion sensing data (202) can expand the functionality of the hearable (102) to include activity detection (208) and / or activity classification (210). Activity detection (208) and / or activity classification (210) can provide additional contextual information for other use cases associated with audio plethysmography, either of which can be used to control the operation of the hearable (102) and / or computing device.

A portable lamp 100, preferably a headlamp 100, which is adapted to be worn or carried by a user, comprising: at least one light source 114, an AI unit 120, wherein the AI unit 120 comprises an activity classification unit 122 and a control unit 124, wherein said activity classification unit 122 is able to automatically classify an activity which the user is currently carrying out without any manual setting by the user, wherein said control unit 124 is adapted to control the beam of said at least one light source 114 at least based on the classified activity of the user.

LABORATORY EQUIPMENT FOR PERFORMING STRUCTURE ACTIVITY CLASSIFICATION OF SMALL MOLECULES

A system and method for automatically classifying an activity of a user 102 during a proposal by an agent 104 to a user based on micro-expression and emotion of the user that provides a succeeding response to the agent 104 such that the proposal becomes successful using an artificial intelligence model is provided. The system includes a facial micro-expression unit 106, an expression analyser 110, the artificial intelligence model 112. The facial micro-expression unit 106 captures an interactive sequence of audio-visual information. The expression analyser 110 processes the interactive sequence of audio-visual information using the artificial intelligence model to determine an emotion and intensity of emotion of the user. The expression analyser 110 creates a record of a set of questions and responses. The expression analyzer 110 provides the succeeding response to the agent based on the created record using a wearable device 114.

1. The name of the design product: active interactive graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: used in the education scene, the man-machine interactive graphical user interface through AI for active interaction. 6. The man-machine interaction mode of the graphical user interface: the card in the front view is an activity classification module, clicking any module enters change state figure 1, the interface shows the theme under the activity classification, clicking any theme enters change state figure 2, the interface shows the theme activity start countdown, after the countdown is over, change state figure 3 is entered, the interface on both sides is an activity reference video for guiding the user to perform related actions, the middle is a real-time shooting video for verifying whether the user's action meets the requirements, when the activity is about to end, change state figure 4, the activity end prompt interface, after the activity is over, change state figure 5, the report display window of related activity data is entered.

Techniques for automating workflow using a real-time driver activity classification model are described herein. For example, an electronic device can execute a machine learning model based at least in part on an input comprising geolocation data and motion data detected by the electronic device. The machine learning model can generate a classification indicating that a state of the electronic device with respect to motion. The electronic device can detect that the electronic device is within a threshold distance from a service address. The electronic device can detect that the state is a non-driving state. Responsive to (i) detecting that the state is a non-driving state and (ii) detecting that the electronic device is within the threshold distance from the service address, the electronic device can transition from presenting a set of driving instructions to presenting instructions for performing a service at the service address.

The invention relates to the technical field of remote sensing monitoring, and discloses a high-position loose body multi-scale deformation monitoring method based on optics and SAR, and aims to solve the problems of poor continuity and accuracy of the existing method, and the scheme mainly comprises the following steps: obtaining a multi-temporal optical image, an SAR image and auxiliary data, and carrying out standardized preprocessing; extracting a horizontal displacement field from the optical image through an improved frequency domain cross-correlation algorithm, and obtaining a time sequence deformation field from the SAR image by using an optimized time sequence InSAR technology; performing spatial adaptive grid fusion and Gaussian process-based time interpolation on the two deformation fields, dynamically distributing fusion weights according to deformation magnitude, topographic conditions and data quality, and generating a time-space continuous comprehensive deformation field; automatic classification of deformation modes is realized through principal component analysis and a K-means clustering algorithm, activity levels are defined in combination with conversion of the deformation modes before and after an earthquake, and a deformation type graph and an activity classification graph are output. According to the invention, the continuity and accuracy of high-position loose body monitoring are improved.

A method and system are provided for monitoring operations of heavy equipment having an operating implement configured to excavate a load from a mine face. The method involves: capturing a plurality of images during an operating cycle of the heavy equipment; processing the plurality of images through an activity classifier model, the activity classifier model including a neural network having been configured and trained for generating an activity label based on a sequence of images in the plurality of images, the activity label associating the sequence of images with at least one stage of a plurality of stages making up the operating cycle; and performing at least one analysis using the sequence of images associated with the at least one stage, the at least one analysis including an analysis of the operating implement, an analysis of the load, and / or an analysis of the mine face.

One embodiment provides a sensor module for patient monitoring. The sensor module includes a processor circuitry; a memory circuitry; at least one time of flight (TOF) sensor; a TOF logic; and a monitor logic. The TOF logic is configured to determine a sequence of elevation maps of at least a portion of a patient room. The TOF logic is further configured to at least one of a distance vector and a velocity vector associated with a selected room occupant based, at least in part, on a plurality of elevation maps in the sequence of elevation maps. Each elevation map is determined based, at least in part, on a respective TOF data set captured from the at least one TOF sensor. Each TOF data set is captured, periodically at a time interval. The monitor logic is configured to classify an activity of the selected room occupant as acceptable or unacceptable. The classifying is based, at least in part, on the at least one of the distance vector and the velocity vector.

To provide a medical system with physiological sensors.SOLUTION: In an example method, a computer system receives, from a wearable medical sensor, acceleration data indicating physical activity of a subject, activity classification data, sleep data, and one or more vital sign metrics. The system determines a physical activity score, a sleep score, and a vital signs score based on the acceleration data, the activity classification data, the sleep data, and the one or more vital sign metrics. The system determines a quality of life score for the subject based on the physical activity score, the sleep score, and the vital signs score; and the system causes the quality of life score to be presented to the subject using a display screen.SELECTED DRAWING: Figure 1

Disclosed herein are embodiments for implementing active illumination control via activity classification. An embodiment includes a processor configured to perform operations comprising receiving first sensor data generated by at least one of the plurality of sensors. Based at least in part on the first sensor data, the processor may select a first lighting profile, and instruct the light-emitting element to emit light in accordance with the first lighting profile. The processor may be further configured to receive second sensor data generated by the at least one of the plurality of sensors and to update an activity classification stored in a memory, in response to the second sensor data being different from the first sensor data. The processor may transition from the first lighting profile to a second lighting profile, in response to the updating, and may instruct the light-emitting element to emit light in accordance with the second lighting profile.

The present invention discloses a multispectral stem cell activity detection and analysis system, and relates to the field of stem cell detection, the system comprises: a spectral image acquisition module for performing image acquisition to obtain multispectral image data; the image processing and correcting module is used for preprocessing the data and obtaining a multispectral image data set; the spectral feature extraction module is used for extracting spectral feature vectors to generate a multi-modal feature parameter set; the stem cell activity identification module is used for constructing a stem cell activity identification model and identifying stem cell activity data; the activity label adding module is used for adding activity classification labels; the activity dynamic analysis module is used for analyzing stem cell activity change trend parameters and generating activity time sequence decay parameters; and the comprehensive evaluation output module is used for generating a stem cell activity grade evaluation report. The whole-process stem cell activity detection and analysis system is constructed through a modular architecture, and the intelligence and dynamic adaptive capacity of stem cell activity detection are improved.

Mechanisms are provided for decentralized security orchestration. The mechanisms execute training, for each vendor network in the plurality of vendor networks, on a corresponding vendor network artificial intelligence (AI) computer model. The training is executed with training data collected from a corresponding vendor network over time, where the training data represents transactions occurring within the corresponding vendor network. The trained models are executed on new security information to thereby classify activity in portions of the data processing system as to a security risk level. Fuzzy logic is executed on the classifications to automatically determine if segmentation of the data processing system is to be performed. If segmentation is to be performed, the mechanisms segment / isolate portions having a predetermined security risk level classification.

A powered prosthesis includes a high-level activity classifier to detect transitions among a plurality of distinct user activities and switch prosthetic joint control to an associated activity controller. The activity classification is reduced to four states with easily distinguishable features to control transitions among more than four activities by not separately classifying upward and downward incline walking or stand-to-sit transitions. The prosthesis has an inter-leg transition accuracy over 99% under both self-paced and rapid-paced fatiguing conditions with a 100% recovery rate due to backup logic or user-cued resets.

The application provides a human activity recognition model training method and recognition method containing confusing activities, the training method comprises the following steps: training a neural network model based on a plurality of point cloud frame sequences carrying human activity classification labels, so as to train the neural network model into a human activity recognition model used for outputting classification results according to the point cloud frame sequences carrying human activity classification labels; wherein the point cloud frame sequences carrying human activity classification labels are obtained based on initial point cloud frame sequences; the neural network model is obtained in advance according to a pre-trained human activity understanding and reconstruction model, the human activity understanding and reconstruction model comprises sequentially connected understanding modules and reconstruction modules; the neural network model comprises sequentially connected understanding modules after knowledge transfer and a classifier. The application can accurately recognize various human activities under the premise of non-contact and non-invasion of privacy by using the human dynamics associated with the point cloud sequence learned in the pre-training stage.