210 results about "Activity recognition" patented technology

The invention discloses a low-power-consumption warning ground pile awakening method and system based on human activity recognition, and belongs to the technical field of image analysis and intelligent security and protection. According to the method, in a micro-power-consumption mode, the environment is monitored through an image sensor, and when environment changes meet awakening conditions, lightweight human body detection is conducted through an edge AI chip. And if the human activity probability exceeds the confidence coefficient, starting a main camera and a high-computing-power AI chip to carry out deep behavior analysis, fusing a behavior analysis result with a geographic position and a timestamp, generating a risk decision result, and triggering a dynamic response. According to the invention, a hierarchical wake-up and multi-source fusion technology is adopted, on-demand work is realized, power consumption is greatly reduced, early warning accuracy is improved through deep behavior analysis, and the problems of high energy consumption and inaccurate early warning of traditional equipment are effectively solved.

System and methods are provided for mitigating click farm fraud by receiving network data and sensor data from a plurality of computing devices, extracting one or more features from the sensor data and the network data for each of the devices. The features represent one or more of a local physical environment and communication channel environment associated with a device of the plurality of computing devices. The method includes determining one or more subsets of the plurality of computing devices based on environmental and network characteristics of the one or more features, identifying, based on the one or more subsets and detected influencer activities, co-located computing devices, and responsive to determining that a count of the co-located computing devices is greater than a predetermined count, sending a session terminating command to one or more servers in communication with the co-located computing devices to mitigate click farm fraudulent activities.

The invention relates to the technical field of wearable exoskeleton assistance, in particular to a lower limb exoskeleton knee joint assistance control method and system. The lower limb exoskeleton comprises a pair of swing arms respectively connected with thighs and shanks and an actuator, and the method comprises the following steps: processing data of a motion parameter sensor, acquiring detection data input by the motion parameter sensor arranged on the lower limb exoskeleton, and processing the detection data to extract motion features; gait phase detection is conducted, the current gait phase is determined according to the motion characteristics, the gait phase comprises a supporting phase and a swinging phase, the supporting phase corresponds to the state that the lower limb touches the ground, and the swinging phase corresponds to the state that the lower limb leaves the ground; activity identification: motion features are extracted in the supporting phase to determine activity types, and the activity types comprise at least one of flat walking, upward walking, downward walking, running, squatting, backward walking and unknown; according to the control strategy, the corresponding control strategy is selected according to the gait phase and the activity type so as to control an actuator.

The invention discloses an activity identification method based on a variational auto-encoder and a sequential network, and belongs to the technical field of behavior feature identification, and the method comprises the following steps: S1, collecting an original data set of an activity signal, and carrying out the preprocessing of the original data set; s2, extracting potential features by adopting VAE based on the S1, and outputting a reconstruction error; s3, on the basis of the S1, obtaining time dynamic characteristics by adopting CNN-GRU; s4, performing feature fusion by adopting a self-attention mechanism based on results of S2 and S3, performing soft distribution by adopting a deep embedded clustering layer, and performing classification by adopting a classifier to complete model training; and S5, testing, judging whether the input is OOD data or not, and outputting the category. According to the method, the reconstruction error and the output of the deep embedded clustering layer are judged, so that the OOD data are effectively identified and processed, and the robustness and generalization ability of the model are enhanced; through combination of VAE, CNN, GRU and a self-attention mechanism, spatiotemporal features in IMU signals are comprehensively extracted, activity identification accuracy is improved, time sequence information is effectively reserved, and model training efficiency is improved.

The invention discloses an activity identification system and method based on millimeter wave radar Doppler characteristics, and relates to the technical field of radar signal processing and artificial intelligence. Comprising a human body behavior radar acquisition module, a human body behavior information transmission module, a human body behavior information preprocessing module, a micro-Doppler feature extraction module, a data classification and identification module and a human body behavior information application module which are connected in sequence, a millimeter-wave radar is adopted to emit high-frequency millimeter-wave signals, and information of human body actions is obtained by receiving signals reflected from the surface of a human body; and the human body behavior information transmission module is used for transmitting the collected behavior data of the user to edge equipment or a cloud server.

The invention discloses a federal personalized human activity recognition training method based on a super network, which comprises the following steps that: a server randomly selects a plurality of clients to participate in training, and broadcasts embedded network parameters to the clients; and after receiving the embedded network parameters, the client generates an embedded description vector in combination with the local data set and uploads the embedded description vector to the server. And the server generates corresponding personalized model parameters according to the embedded description vector uploaded by each client, and then issues the personalized model parameters to the clients for local fine tuning. And after fine tuning is completed, the client uploads the personalized model parameter update quantity to the server, and the server updates the super network according to the personalized model parameter update quantity, generates an embedded description vector update quantity and returns the embedded description vector update quantity to the client. And the client generates an update gradient for guiding the embedded network according to the update quantity of the embedded description vector by combining the similarity consistency constraint between the embedded space and the personalized model parameter space. And the server collects the update gradients uploaded by all the clients and aggregates the update gradients to complete the update of the embedded network.

A machine task tutorial system is disclosed that utilizes augmented reality to enable an expert user to record a tutorial for a machine task that can be learned by different trainee users in an adaptive manner. The machine task tutorial system advantageously utilizes an adaptation model that focuses on spatial and bodily visual presence for machine task tutoring. The machine task tutorial system advantageously enables adaptive tutoring in the recorded-tutorial environment based on machine state and user activity recognition. The machine task tutorial system advantageously utilizes AR to provide tutorial recording, adaptive visualization, and state recognition. In this way, the machine task tutorial system supports more effective apprenticeship and training for machine tasks in workshops or factories.

Disclosed herein are systems and methods for monitoring and evaluating a user's gait. In one embodiment, the method comprises training one or more human activity recognition (HAR) models, each HAR model comprising at least one artificial neural network (ANN) trained on a general or phenotype-specific population. The HAR models are used to identify one or more ambulatory activities in sensor data measured by one or more sensors from a pair of smart insoles worn by the individual. The data is segmented into one or more segments in accordance with the identified ambulatory activities. A gait detection algorithm is used to characterize a gait event with one or more spatiotemporal metrics. The spatiotemporal metrics are classified via one or more machine learning algorithms to produce a gait quality index (CI) score.

The invention relates to the technical field of human activity recognition, and particularly discloses a human activity recognition method and system based on a lightweight hybrid neural network and self-attention migration and a medium thereof, and the method comprises the steps: 1, employing an HHAR data set, and carrying out the preprocessing; step 2, constructing a teacher model based on a CNN-LSTM-Transform hybrid architecture, and constructing a teacher model based on the CNN-LSTM-Transform hybrid architecture; step 3, constructing a student model of a hybrid architecture based on CNN-LSTM-Transform; and step 4, utilizing a self-attention migration mechanism to guide the student model to learn the attention distribution mode of the teacher model, and realizing efficient migration of knowledge. According to the method, the calculation complexity and the storage requirement can be remarkably reduced while the expression ability of the model is maintained.

The invention relates to a multi-mode human body activity identification method based on WIFI and videos, and the method comprises the steps: S1A, processing WIFI data, and obtaining WIFI space features; s1B, extracting human skeleton information in the video image to obtain visual features; s2A, performing spatial processing on the visual spatial features and the WIFI spatial features to obtain fused spatial features; s2B, performing time processing on the visual spatial features and the WIFI spatial features to obtain fusion time features; and S3, analyzing the fusion space feature and the fusion time feature to obtain a human body activity type. The multi-mode human body activity identification method based on the WIFI and the video has the advantages of being real-time and low in power consumption.

The invention discloses an agricultural environment perception and robot-assisted picking method based on vision, and the method employs an end-to-end detection, tracking and classification frame, comprises five independent modules: object detection and tracking, feature extraction, activity recognition, target position estimation, and a robot scheduler, obtains information through employing an RGB camera and a two-dimensional laser radar, and carries out the detection and classification of the information. By customizing long and short-term memory network processing data, compared with other methods, an end-to-end architecture adopted by the method can enable the system to comprehensively process the whole process from activity detection of a picker to auxiliary operation of a robot, and adopted multi-camera information fusion can accurately estimate the positions of the picker in different distance ranges, so that the accuracy of picking is improved. And the behavior activity of the picker can be accurately predicted by adopting the customized long-short-term memory network.

The invention discloses a human body activity identification method, system and device, relates to the field of terminals and communication, and is used for solving the problem of low accuracy of human body activity identification in the prior art. Comprises: acquiring original data; preprocessing the original data to obtain preprocessed data, and performing feature extraction on the preprocessed data to obtain target features; performing data-level fusion on the original data, performing feature-level fusion on the target features, and performing decision-level fusion on the feature fusion model to obtain fusion information; and performing human body activity identification based on the fusion information. According to the technical scheme, the flexibility, the anti-interference capability and the prediction precision of the prediction model can be improved, and human body activity identification can be performed on the target user more efficiently and accurately; and moreover, the calculation complexity and occupied hardware resources of a human body activity recognition algorithm are reduced by small flux, so that the energy consumption and response time of the human body activity recognition method are reduced.

Systems and methods presented herein include one or more guest activity recognition devices configured to recognize activity of one or more guests within a physical environment of an amusement park. The system also includes one or more ride vehicles of a ride of the amusement park, each ride vehicle including an artificial intelligence entity management system configured to maintain one or more ride vehicle artificial intelligence entities of the ride vehicle based at least in part on the recognized activity of the one or more guests; and one or more features disposed on the ride vehicle and configured to be activated by the artificial intelligence entity management system to simulate the existence of the one or more ride vehicle artificial intelligence entities in accordance with one or more properties of the one or more ride vehicle artificial intelligence entities.

The invention specifically discloses an omni-directional human body activity recognition method and device based on an HDMR, a computer and a medium, and relates to the technical field of radar signal processing. According to the method, an HDMR-based synthetic data generation algorithm is utilized, data expansion can be carried out on collected samples in a radar zero-degree observation angle direction and a small number of samples in other non-zero-degree observation angle directions, and high-quality training data containing all observation angle directions are generated; the objective of the invention is to solve the problem of angle sensitivity of a monostatic radar in omni-directional human body activity recognition. In addition, according to the invention, the dynamic time warping distance DTWD is adopted to measure the similarity between the synthetic sample and the real sample, so that the quality of the synthetic sample is evaluated. And finally, inputting the synthesized sample data in different observation angle directions into a CNN classifier based on ResNet50 for training so as to realize omnidirectional human body activity recognition.

The invention discloses an activity identification method, system and equipment based on dual-band signal comparative learning. The method comprises the following steps: synchronously acquiring channel state information (CSI) signal data X24 and X5 of dual bands through multi-antenna Wi-Fi equipment; processing acquired dual-band CSI signal data X24 and X5 through a residual neural network model based on comparative learning and multi-task joint training to generate dual-band vectors f24 and f5, and performing dynamic attention weighted feature fusion to generate a fusion feature; and inputting the fusion feature into a classifier of the residual neural network model for activity identification and classification, and then outputting an identification result. The method supports high-robustness multi-person activity recognition, and is suitable for an intelligent sensing scene in a dynamic environment.

The invention discloses an activity identification method based on conditional antagonism data enhancement, which comprises three parts of a conditional generative adversarial network, a kinematic feature constraint module and a prototype sample generator, a system hardware structure is designed based on an embedded heterogeneous computing platform, a main processor adopts an ARM Cortex-A72 core operation condition generative adversarial network, and the model sample generator is designed based on an embedded heterogeneous computing platform. The coprocessor adopts NPU to accelerate kinematics parameter calculation, and the sensor interface module is connected with a six-axis IMU sensor through an SPI bus to collect data of an accelerometer and a gyroscope in real time. According to the activity recognition method based on conditional antagonism data enhancement, enhancement data with diversity, authenticity and semantic accuracy can be provided for a human body activity recognition model in a scene of insufficient training data, so that the overall recognition accuracy of the model on a UCI HAR data set is improved by 14.7%, the F1 value on the aspect of few sample categories is averagely improved by 21.3%, and the recognition accuracy of the model on the UCI HAR data set is greatly improved. And an effective technical solution is provided for data scarce scenes such as medical monitoring and motion analysis.

The invention relates to the technical field of live pig feeding control. The invention aims at providing an intelligent feeding control system based on the Internet of Things. The intelligent feeding control system comprises a central server and a plurality of terminal feeding subsystems which are in communication connection with the central server through relay communication boxes. The terminal feeding subsystem is arranged in a pigsty and comprises an image acquisition device; a pig identification device; a feeding device; and a repelling mechanism. Precise feeding can be carried out on the individual pigs according to the actual conditions such as the body shapes and the back fat of the pigs, and the conditions of insufficient feeding and excessive feeding are avoided. The system adopts the central server as a localized intelligent control center, has a real-time figure and backfat monitoring function, a pig high-precision identification function, a pig activity identification function, an individual adaptive feeding function, an overeating expelling function and the like aiming at individual pigs, and integrally realizes automatic high-precision intelligent feeding; and the method has positive significance on optimal management of modern pig farms.

The invention discloses a millimeter wave radar point cloud-based human body activity identification data enhancement system and method, and relates to the technical field of human body activity identification, and the method comprises the steps: collecting original point cloud data of human body activity, and carrying out the preprocessing; performing distance translation operation on the point cloud according to a radar signal-to-noise ratio calculation formula; based on a deep learning framework, training a variational auto-encoder model, generating a point cloud set after the noise is added for the first time, performing down-sampling on a continuous frame set corresponding to the point cloud set, and correcting the speed of generating a sample; performing angle rotation operation on the point cloud set after speed simulation according to the characteristic that the antenna gain changes along with the angle; establishing a physical noise model, and performing feature enhancement on the point cloud set after angle rotation; and training and evaluating the human body activity identification model. Therefore, by adopting the method, richer and more diversified data fitting the actual scene can be generated, so that the generalization ability and the recognition accuracy of the human body activity recognition model in the complex and changeable scene are effectively improved.

The invention discloses a substation personnel management and control method and system based on a digital twinborn model, and a storage medium. The method comprises the steps of obtaining positioning information of substation personnel and mapping the positioning information to the digital twinborn model of a substation; based on the analysis of a digital twinborn model, screening out to-be-identified personnel in the substation personnel, and determining the identification type of each to-be-identified personnel; if the identification type is permission identification, acquiring biological feature information of the to-be-identified person for identification, and acquiring a feature identification result to control access permission of the to-be-identified person; and if the identification type is activity identification, obtaining an activity track of the to-be-identified person to perform identification, and obtaining a track identification result to perform management and control on activity normalization of the to-be-identified person. According to the invention, the to-be-identified personnel are distinguished and identified according to the respective determined identification types, and distinguished management and control are carried out according to the identification result, so that the safety of the personnel entering the transformer substation is ensured, and safe operation of the transformer substation is facilitated.

The invention discloses a WiFi signal human body activity identification method based on a time sequence alignment network, and relates to the technical field of human body activity identification. Comprising the following steps: acquiring channel state information; dividing into a training set and a test set; constructing a time sequence alignment network model, introducing a deep large-kernel convolution enhanced backbone network to extract depth feature tensors, respectively inputting the depth feature tensors into a double-branch structure, performing local feature processing on an upper branch, converting the feature tensors into global feature vectors through global average pooling operation on a lower branch, and outputting the global feature vectors; performing joint optimization on the time sequence alignment network model by adopting a cross entropy loss function and a ternary loss function, and obtaining a trained time sequence alignment network model after a plurality of times of training; and inputting the test set into the trained time sequence alignment network model, and evaluating a human body activity identification result. According to the method, local feature dynamic matching is carried out on the depth features extracted by the backbone network, so that actions occurring in different time sequences are aligned, and the recognition accuracy is further improved.

Aspects of the present invention relate to a control system (1) for identifying at least a first activity performed by a person of interest (POI-n). The control system (1) includes a controller having a processor (20) and a system memory (26). The controller (13) is configured to receive image data (DIMG1, DIMG2) from at least one imaging device (C1, C2). The image data (DIMG1, DIMG2) includes at least one image frame representing an image scene including at least a portion of a person of interest (POI-n). The image data (DIMG1, DIMG2) is analysed to identify a skeletal model (15) of the at least a portion of the person of interest (POI-n); and at least one object of interest (OOI-n) with which the person of interest (POI-n) is interacting. The first activity being performed by the person of interest (POI-n) is identified in dependence on the skeletal model (15) and the identified object of interest (OOI-n). The present invention relates to a related method of identifying an activity being performed by a person of interest (POI-n). The present invention also relates to computer software and a non-transitory, computer-readable storage medium storing instructions thereon.

The present disclosure relates to a custom framework for fine-grained human activity recognition. One or more input videos may be accessed, where the one or more input videos comprise one or more frames depicting one or more actors and one or more objects. A plurality of object-pose interaction graphs may be generated for individual frames from the one or more input videos based at least in part on one or more objects of interest from the one or more objects and on one or more joint keypoints of the one or more actors. A first graph neural network may be trained based at least in part on the plurality of object-pose interaction graphs to identify spatial information for the one or more actors, the one or more objects of interest, and one or more interactions between the one or more actors and the one or more objects of interest. A second graph neural network may be trained based at least in part on the plurality of object-pose interaction graphs and one or more keyframes from the plurality of frames to identify temporal information for the one or more actors, the one or more objects of interest, and the one or more interactions between the one or more actors and the one or more objects of interest. A classifier may be trained to identify one or more actions in the one or more input videos based at least in part on the spatial information and the temporal information.

The invention discloses a hierarchical human body activity recognition method and system based on multiple position sensors. The system comprises a plurality of sensor modules, a data fusion module, a feature vector extraction module, a time sequence feature generation module and a human body activity recognition and judgment module. The plurality of sensor modules are used for acquiring and collecting detection data; the data fusion module performs data layer fusion on the collected detection data; a feature vector extraction module extracts representation vectors matched with human body activities from the fused data; a time sequence feature generation module generates hierarchical time sequence features of the human body activity according to the representation vector matched with the human body activity and the time change; the human body activity recognition and judgment module judges and recognizes human body activities according to the time sequence characteristics of the hierarchical human body activities. According to the method, a series of recognition technologies such as data fusion, feature extraction and action recognition are adopted to quickly recognize human body actions, and the actions are analyzed by adopting a plurality of sections of continuous time axes, so that subsequent action prediction is facilitated.

A computer system is disclosed for processing an activity class signal comprising a dominant activity class and a plurality of less dominant activity classes. A runtime activity class detector is trained to detect the dominant activity class in the activity class signal, and a false positive (FP) filter is configured to filter out FP classifications detected by the runtime activity class detector, wherein the FP filter is trained based on the less dominant activity classes in the runtime activity class signal.

An exemplary virtual IMU extraction system and method are disclosed for human activity recognition (HAR) or classifier system that can estimate inertial measurement units (IMU) of a person in video data extracted from public repositories of video data having weakly labeled video content. The exemplary virtual IMU extraction system and method of the human activity recognition (HAR) or classifier system employ an automated processing pipeline (also referred to herein as “IMUTube”) that integrates computer vision and signal processing operations to convert video data of human activity into virtual streams of IMU data that represents accelerometer, gyroscope, or other inertial measurement unit estimation that can measure acceleration, inertia, motion, orientation, force, velocity, etc. at a different location on the body. In other embodiments, the automated processing pipeline can be used to generate high-quality virtual accelerometer data from a camera sensor.

Various arrangements for monitoring for contactless human interactions and health using a single radar transmission modulation mode are provided. Radar chirps may be output by a radar sensor operating in a burst mode. The burst mode radar data stream may be monitored for a contactless human interaction performed by an active user. The burst mode radar data stream may be converted to a virtual continuous mode radar data stream. Health monitoring of an inactive user may be performed using the virtual continuous mode radar data stream.

The application discloses a CSI-based position-independent human activity continuous learning recognition method, which comprises the following steps: 1, collecting CSI action sample data; 2, pre-processing the CSI action sample data; 3, constructing positive samples by randomly scaling the pre-processed samples in the time dimension; 4, constructing a multivariate time graph neural network and extracting CSI action sample features; 5, calculating the similarity between the sample feature values and the positive samples and the feature values of the remaining samples, obtaining a comparison loss, and optimizing the feature extraction network; 6, freezing the feature extraction network, sending the features obtained from the input samples into a classifier for training to obtain a classification model. When the application continuously learns new action categories, the user does not need to retrain the feature extraction network, and the new and old action recognition in any position in the room can be realized by providing limited position new category samples to train the classifier, and the practicability is relatively high.

The invention relates to a human body activity identification method based on a bidirectional cross-modal attention mechanism, and the method comprises the following steps: firstly, collecting an activity data set (such as millimeter wave radar data, RGB image data and the like) of a user at the same time through employing a plurality of wireless devices, carrying out the preprocessing, and dividing the training data into unmarked data and marked data; training a pre-training model built based on an unsupervised confrontation contrast learning technology by adopting unmarked data; thirdly, finely adjusting the model based on the bidirectional transmembrane state attention mechanism by using the mark data to obtain a human body activity recognition model; and finally, inputting multi-modal data in a user or scene which is not used for training into the model to finish accurate recognition. The domain adaptivity of the model is improved through an adversarial contrast learning method, and a bidirectional cross-modal attention mechanism is designed to enhance information fusion between modals, so that the method is suitable for human body activity recognition tasks with only a small number of labeled samples in various complex environments.

A system for recognizing human activity from a video stream includes a classifier for classifying an image frame of the video steam in one or more classes and generating a class probability vector for the image frame based on the classification. The system further includes a data filtering and binarization module for filtering and binarizing each probability value of the class probability vector based on a pre-defined probability threshold value. The system furthermore includes a compressed word composition module for determining one or more transitions of one or more classes in consecutive image frames of the video stream and generating a sequence of compressed words based on the deter-mined one or more transitions. The system furthermore includes a sequence dependent classifier for extracting one or more user actions by analyzing the sequence of compressed words to and recognizing human activity therefrom.

Implementations generally use multiple cameras to recognize clinical activities. In some implementations, the method includes acquiring multiple videos of multiple objects in an environment. The method further includes determining one or more key points for each object of the multiple objects. The method further includes recognizing activity information based on the one or more key points. The method further includes calculating workflow information based on the activity information.