78 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.

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion can be used for motion-artifact filtering. With motion-artifact filtering, noise caused by motion of a user can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion can expand a functionality of a hearable to include activity detection and / or activity classification. While activity detection and / or activity classification can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable and / or a computing device.

Various embodiments provide a system, method, and device for applying a DNS activity classification framework. The method incudes (i) collecting DNS-related activity, (ii) determining whether the DNS-related activity is associated with a DNS tunneling campaign or tool, and (iii) performing an active measure in response to detecting DNS traffic associated with a tunneling domain.

Methods and systems for supporting and / or assisting users' movements include detecting, by one or more sensors integrated with a garment having two or more controllable regions, movement of a particular part of a user's body enclosed within the garment, measuring, by the one or more sensors, data that indicates the detected movement of the particular part of the user's body, determining, based on the measured data, an activity classification that indicates a future movement of the particular part of the user's body, identifying, based on the determined activity classification and by the one or more processors, a support configuration for the garment, and dynamically adjusting, by the one or more processors, at least one of an effective tension and an effective stiffness of at least one of the two or more controllable regions of the garment to provide the identified support configuration of the garment.

A system for monitoring animal activity, the system comprising an animal tag having a tag identifier attached to an animal to generate animal data packets comprising timestamped location data having tag latitudinal and longitudinal coordinates and timestamped activity data relating to activity of the animal over a window of time and a processor configured to evaluate the animal data packets to determine an activity classification for the animal wearing the animal tag over the window of time. The processor is also configured to calculate a pasture performance value of the area based on the animal data packets received from the animal tag.

Techniques and apparatuses are described that perform audioplethysmography and motion-sensing data fusion. Fusing motion-sensing data with audioplethysmography expands the situations in which audioplethysmography can operate. In one aspect, audioplethysmography and motion-sensing data fusion (202) can be used for motion-artifact filtering (206). With motion-artifact filtering (206), noise caused by motion of a user (106) can be attenuated to improve sensitivity and accuracy for audioplethysmography. This performance improvement expands the ability of audioplethysmography to support use cases during situations in which the user (106) engages in an activity. In another aspect, audioplethysmography and motion-sensing data fusion (202) can expand a functionality of a hearable (102) to include activity detection (208) and / or activity classification (210). While activity detection (208) and / or activity classification (210) can provide additional contextual information for other use cases associated with audioplethysmography, either can also be used to control an operation of the hearable (102) and / or a computing device.

The invention discloses a non-contact real-time monitoring method for human respiration in a Wi-Fi (Wireless Fidelity) complex interference environment. And analyzing the CSI interference intensity and the CSI active ratio, constructing a Wi-Fi interference feature mapping matrix, and calculating each channel interference index by using the matrix to realize interference discrimination. Then, through a dynamic subcarrier selection algorithm CSI-DSSA based on an interference index, a subcarrier combination with the weakest cross correlation in interference data is selected for interference processing, and time domain feature information of multiple data streams in data which is not interfered in a multi-link data fusion method aggregation mode is analyzed. And finally, extracting a time domain characteristic value and constructing an SVM multi-activity classification model in a Wi-Fi interference environment to obtain a tumble activity identification result. According to the method, the human respiratory activity recognition accuracy in the Wi-Fi interference environment can be effectively improved.

Methods, apparatus, and processor-readable storage media for determining security risks related to local administrator rights (LAR) activity are provided herein. An example computer-implemented method includes obtaining data pertaining to one or more activities performed by at least one user acting in connection with at least one granted set of LAR; classifying the one or more activities into one or more security risk-based categories by processing at least a portion of the obtained data; determining one or more security-related recommendations based at least in part on the classifying of the one or more activities into the one or more security risk-based categories; and performing at least one automated action based at least in part on at least a portion of the one or more security-related recommendations.

A computer-enabled system and method that collects and processes various data from mobile devices possessed by on-demand cargo delivery transportation drivers, along with other possible external data sources, and subsequently recognizes, classifies, stores, and delivers cargo delivery-related activity information of individual or multiple drivers to various consumers of the system, while also iteratively improving the system and method's cargo delivery activity classification accuracy over time.

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.

The invention provides a radar-based full-time in-bed state monitoring method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining target existence information and target physiological information; performing feature extraction on the target existence information and the target physiological information, and adaptively dividing a daytime period and a night period based on a feature extraction result; performing multi-mode sleep state staging based on the target existence information and the target physiological information in the night time period; performing daytime activity state classification based on the target existence information and the target physiological information in the daytime period; and generating a full-time in-bed state monitoring report based on the sleep state staging result and the daytime activity state classification result. According to the method provided by the invention, both long-term bedridden people and non-bedridden people can be considered, the day and night time periods are automatically switched, and the function that single radar equipment synchronously supports night sleep staging and daytime activity classification is realized.

The invention discloses an activity classification method and system supporting RPA and based on a graph neural network. According to the method, the clustering operation is performed according to the output reconstruction features, so that the problems of multi-track staggering in the user operation behavior log and poor classification effect when a complex process exists can be effectively solved. When the model is constructed, the edge and node information is fused into an attention mechanism, the relation between the nodes and the edges of the context and more complex semantic information are comprehensively considered in the obtained message feature representation, and the obtained reconstruction vector can more accurately represent the interaction between the activities; therefore, the complex track tasks of the user operation behavior logs can be classified correctly.

The invention provides a knee joint exoskeleton multi-action torque auxiliary control method based on activity classification and phase detection. The knee joint exoskeleton multi-action torque auxiliary control method comprises a two-stage artificial neural network and three human body dynamic models. The first stage of the two-stage artificial neural network is composed of a network used for activity classification, the second stage of the two-stage artificial neural network is composed of three independent gait phase estimation regression networks, and the three human body dynamic models comprise a single-support human body model, a double-support human body model and a swing human body model which are divided according to human body gait phase features. The model outputs the real biological joint torque at the current moment, and the controller proportionally outputs an auxiliary torque curve required by the exoskeleton. According to the method, four activity types of standing, walking, going upstairs and going downstairs can be accurately classified, the gait phase percentage under the non-rhythm gait condition is accurately estimated, and meanwhile, the corresponding auxiliary torque curve is generated based on the current gait type and gait phase.

The invention provides a training method and a recognition method for a human body activity recognition model containing easily-confused activities, and the training method comprises the steps: training a neural network model based on a plurality of point cloud frame sequences carrying human body activity classification labels, the neural network model is trained into a human body activity recognition model used for correspondingly outputting a classification result according to a point cloud frame sequence carrying a human body activity classification label; wherein the point cloud frame sequence carrying the human body activity classification label is obtained based on the initial point cloud frame sequence; the neural network model is obtained in advance according to a human body activity understanding and reconstruction model obtained through pre-training, and the human body activity understanding and reconstruction model comprises an understanding module and a reconstruction module which are connected in sequence; the neural network model comprises a knowledge-migrated understanding module and a classifier which are connected in sequence. According to the invention, on the premise of non-contact and no privacy invasion, various human body activities can be accurately identified by using the human body dynamics associated with the point cloud sequence learned in the pre-training stage.

The invention discloses a flexible marketing activity creation method based on a background arrangement engine. The method comprises the following steps: creating activity classifications, and creating specific marketing activity types under the created activity classifications; according to the method, activity components and right components are newly added and managed, parameters and rules of the activity components and the right components are configured, each activity component and each right component are realized as independent modules, and the components are mutually independent and randomly combined through the parameters and the rules of the components stored in a database; the activity components of the multiple activity playing methods and rules are combined and configured, and final configuration of the activity is generated; a prize issuing rule of the activity is configured, and secondary arrangement of a prize system is carried out; and creating a specific marketing activity, and displaying the basic information, the participation condition and the effect data of the marketing activity. According to the method, the flexibility and diversity of marketing activities are improved, the flexibility and operability of prize management are enhanced, the effectiveness and participation degree of the activities are improved, and a marketing strategy is optimized.

The invention provides a cold start method of a brain-computer interface, which relates to the technical field of brain-computer interfaces, and comprises the following steps: collecting an initial signal of a target user by using brain-computer interface equipment; the initial signal comprises electroencephalogram data and magnetoencephalogram data; preprocessing the initial signal to obtain a preprocessed signal; performing feature extraction on the preprocessed signal to obtain a signal feature; inputting the signal features into a trained electroencephalogram activity classification model to obtain a classification result; and generating a feedback result according to the classification result, and sending the feedback result to the target user. The cold start method of the brain-computer interface effectively solves the problems that a system based on transfer learning is poor in adaptive capacity and slow in response in the cold start stage of a new user. According to the method, individuation, real-time feedback and online learning are emphasized, so that a more effective and flexible user adaptation scheme is provided, the interactive experience of the user is smoother, and the overall performance of the system and the user satisfaction degree are improved.

A system and method to obtain activity data for an activity of a user, the activity data corresponding to multiple parameters. A set of threshold criteria that corresponds to a multi-parameter activity zone is retrieved, where the set of threshold criteria includes an effort threshold criteria related to a heart rate of the user. An effort of the user is determined based on the activity data. A resistance experienced by the user is determined, where the resistance includes a measure representing at least one of stride rate, step rate, steps, or cadence. One or more environmental conditions associated with the user are determined. Based on the set of threshold criteria corresponding to the multi-parameter activity zone, an instance of the activity is classified as an activity type based on the resistance experienced by the user, the effort of the user, and the one or more environmental conditions associated with the user.

Systems and methods are provided to classify activities into clusters. The systems and methods access data representing activity on a network site and generate, based on the data, a plurality of feature sets representing periods of activeness on the network side. The systems and methods form a subset of the plurality of feature sets by reducing dimensionality of the plurality of feature sets. The systems and methods generate a plurality of clusters of the subset of the plurality of feature sets, each cluster being associated with a label representing a different type of activeness on the network site, and generate a database query set based on the plurality of clusters and the plurality of feature sets to classify one or more activities on the network site into one of the plurality of clusters.

The invention discloses a human body activity identification method and system capable of effectively capturing variable time-space relations in sensors and between the sensors. The method comprises the following steps: 1) carrying out cross-variable fusion on local cross-channel fusion features; 2) performing global time aggregation on the local cross-variable fusion features to obtain sensor feature data; 3) integrating different sensor feature data by using a self-attention mechanism to generate human body activity features; and 4) inputting the human body activity features into a full-connection linear classifier to obtain human body activity categories. The system comprises N wearable motion sensors, a data conversion module, a local time feature extraction module, a cross-channel fusion module, a cross-variable fusion module, a global time aggregation module, a human body activity feature extraction module and a human body activity classification module. By capturing the space-time relationship between the interior of the sensor and the sensors, the frame can provide more systematic information, so that the model is more accurate during analysis and prediction.

A method for monitoring occupants of a vehicle comprises identifying a respective body region for one or more occupants of the vehicle within an obtained image; identifying within the body regions, skeletal points including points on an arm of a body; identifying one or more hand regions; and determining a hand region to be either a left or a right hand of an occupant in accordance with its spatial relationship with identified skeletal points of the body region of an occupant. The left or right hand region for the occupant are provided to a pair of classifiers to provide an activity classification for the occupant, a first classifier being trained with images of hands of occupants in states where objects involved are not visible and a second classifier being trained with images of occupants in the states where the objects are visible in at least one hand region.

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 include acquisition of a first image of a first activity record, determination of first text based on the first image, generation of a first embedding based on the first text, generation of a second embedding based on the first embedding using a first model, where a number of dimensions of the second embedding is less than a number of dimensions of the first embedding, determination of a first cluster based on the second embedding using a second trained model, the second trained model trained using unsupervised learning, and determination of a first activity type associated with the first activity record based on the first cluster, the second embedding and historical activity data associating the first cluster with a plurality of activity types and each of the plurality of activity types with a respective embedding metric.

Medical systems, methods, computer-readable media, and program products with physiological sensors are provided. In an example method, a computer system receives acceleration data, activity classification data, sleep data, and one or more vital sign metrics indicative of physical activity of a subject from a wearable medical sensor. The system determines a physical activity score, a sleep score, and a vital sign score based on acceleration data, activity classification data, sleep data, and 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 sign score; and the system causes the quality of life score to be presented to the subject using the display screen.

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.

Implementations disclosed describe methods, devices, and systems to perform out-of-distribution and recognition of activities using an inertial measurement unit (IMU) sensor. A method may include receiving motion data by a device from a motion sensor. The method further includes generating image data comprising one or more images based on the motion data. The method further includes determining that a first portion of the image data corresponds to activities outside a classification distribution. The method further includes filtering the image data by removing the first portion from the image and generating filtered image data. The method further includes determining an activity classification, within the classification distribution, based on the filtered image data. The method further includes modifying an operating parameter of the device based on the activity classification.

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.

The present invention provides a multi-action torque assistance control method for a knee exoskeleton based on activity classification and phase detection, including a two-stage artificial neural network and three human body dynamics models; the two-stage artificial neural network, the first stage consists of a network for activity classification, and the second stage consists of three independent gait phase estimation regression networks. The three human body dynamics models include a single-support human body model, a double-support human body model, and a swing human body model divided according to the human gait phase characteristics. The model outputs the true biological joint torque at the current moment, and the controller proportionally outputs the required auxiliary torque curve of the exoskeleton. The present invention can accurately classify four activity types of standing, walking, going up stairs, and going down stairs, accurately estimate the gait phase percentage under non-rhythmic gait conditions, and generate corresponding auxiliary torque curves based on the current gait category and gait phase.

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.