77 results about "Activity recognition" patented technology

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 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.

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.

This invention discloses a method and system for protecting privacy in wearable devices for human activity recognition, aiming to solve the problems of existing methods struggling to balance privacy and utility, and high resource consumption on the edge. The method first acquires multi-channel time-series signals from the wearable device and constructs fixed-length feature vectors. Based on a sensitive attribute inference model and the number of high-risk features, it divides the device into high-risk and low-risk feature sets. Only the high-risk feature sub-vectors are subjected to perturbation noise to generate perturbation latent representations, which are then uploaded to an edge server along with the low-risk feature set. The edge server generates the final feature vectors and assesses the risk of privacy leakage. An offline evaluation is used to construct a privacy-utility trade-off, dynamically adjusting the perturbation dimension and noise amplitude. This invention achieves targeted protection of high-risk features, maximizing the preservation of key recognition information, reducing edge computing power and communication overhead, adapting to the differentiated needs of different scenarios, and balancing privacy security and activity recognition performance.

The application discloses a kind of edge cloud cooperation human activity recognition modeling methods based on heterogeneous multi-modal data, and is divided into two stages of centralized pre-training and multi-modal semi-supervised fine-tuning.In the first stage, by dynamic mask contrast learning, pre-train the basic model based on converter using the joint dataset with super-class information, so that it has the ability to extract robust features from any modal combination.In the second stage, the local unlabeled data is used by the end-side client to generate pseudo-labels through weak mask view and calculate losses based on strong mask view for local update;Cloud side aggregates heterogeneous model parameters from different clients, and fine-tunes the global model using a small amount of labeled data.The application effectively alleviates the data heterogeneity problem by separating single-modal feature encoding and cross-modal information fusion, and can efficiently train a high-performance multi-modal human activity recognition model using a small amount of labeled data and unlabeled data while protecting data privacy.

Embodiments of the present disclosure relate to activity recognition methods utilizing automatic training based on inertial sensors. A technical advance is disclosed that utilizes inertial sensor data associated with a device to determine a new feature array and determine whether the new feature array is within an existing class within a state space associated with the inertial sensor data. In response to the new feature array being included in the existing class, the new feature array is added to the existing class and a representation of the existing class in the state space is updated based on the new feature array and an existing representation of the existing class. In response to the new feature array not being included in the existing class, a new class is created based on the new feature array.

In one embodiment, a method includes accessing a current sensor signal from an inertial measurement unit (IMU) of an earbud in a current earbud orientation worn by a user and accessing a baseline-orientation IMU sensor signal for the earbud in a baseline earbud orientation. The method further includes estimating, for the earbud and based on the current sensor signal and the baseline-orientation IMU signal, an orientation transformation matrix that transforms the current sensor signal from the current earbud orientation to the baseline earbud orientation; and transforming the current sensor signal from the current earbud orientation to the baseline earbud orientation using the orientation transformation matrix.

An object of a system according to an exemplary embodiment is to detect a security threat of a server in real time and quickly deal with the security threat.SOLUTION: A system according to an embodiment includes a real-time analysis unit, a suspicious activity detection unit, a threat handling unit, a report generation unit, and an automatic learning unit. The real-time analyzer analyzes the behavior of the server in real time using the generated AI. The suspicious activity detection unit detects a suspicious activity from the data analyzed by the real-time analysis unit. The threat handling unit identifies and handles a threat based on the suspicious activity detected by the suspicious activity detection unit. The report generation unit generates a security report based on the information on the threat addressed by the threat handling unit. The automatic learning unit automatically learns based on the report generated by the report generation unit to improve the security measure.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data analysis, in particular to an Internet of Things time sequence analysis method based on a self-adaptive hypergraph neural network, which comprises the following steps: acquiring a plurality of human activity identification data, determining a selected data volume according to data fluctuation, determining activity label similarity according to information association degree, and obtaining a time sequence analysis result; according to the activity label similarity, human activity identification data is selected as training data according to the overall similarity and the selection similarity or according to the amplitude change rate; according to the motion complexity of the training data, determining whether to increase and adjust the scale feature level to determine a target scale feature level; and the features of the points are connected through Tensor, and classification is carried out through Linear to obtain a prediction category result corresponding to the training data. The method can improve the recognition precision of the human activity recognition task.

Various embodiments provide methods performed by a user equipment (UE) including receiving sensor data associated with activity of a user from a plurality of inputs at a first-time instance, determining a state of the user at the first-time instance based on the sensor data, and identifying, by the processor, at least one attribute of the user or the activity of the user based on the sensor data and the state of the user collected at multiple time instances including the first-time instance. Identifying the at least one attribute may use an inference model to receive selected sensor data as an input and to provide an output classifying the state of the user at the multiple time instances as an attribute of the user.

An apparatus using a lightweight transformer model for human activity recognition in a portable device, includes: a data collector configured to collect data for human activity recognition based on an mmWave radar; a data processor configured to perform a data processing process to process mmWave data and convert the mmWave data into an input form for a model; a lightweight GST model part configured to generate a feature vector by combining a grouped attention mechanism, which splits an input sequence into several small groups and independently calculates attention within each group, and a sparse attention mechanism, which calculates attention only for selected location pairs rather than calculating attention for all location pairs, to classify an output class through a fully connected layer; and a human activity recognition result outputter configured to output human activity recognition results based on human activity types classified in the lightweight GST model part.

The present subject matter relates to a system (100) and a method (300) for providing automated resolution to one or more anomalous events in an enterprise information technology (IT) environment. The system (100) integrates a processor (201) and a memory (202) that stores instructions to execute various tasks. The system (100) monitors activities within the enterprise IT environment, identifies one or more anomalous events, and correlates the identified anomalous events with one or more predefined resolution workflows. Each workflow includes specific operating instructions tailored to address the identified anomalies. Upon detecting an anomaly, the system (100) extracts the relevant operating instructions from the corresponding workflow and executes them to resolve the issue. Thus, the system (100) significantly reduces the mean time to resolve by automating the detection and resolution of anomalies, thereby improving operational efficiency and minimizing downtime in the enterprise IT environment.

An audio-visual pilot activity recognition system including one or more image collectors, one or more audio collectors, and a processor configured to collect at least one image signal from the one or more image collectors, collect at least one audio signal from the one of more audio collectors, and determine a pilot activity based on the collected at least one image signal and the collected at least one audio signal.

The application discloses a cross-user activity recognition method based on source domain data screening and training feature constraint. The data distribution of different users is calculated through optimal transmission distance, and source domain data screening is performed according to the distribution distance of the users, thereby reducing the problem of poor domain adaptation migration. Data enhancement is performed in the screened source domain data, so that the source domain data distribution is more continuous, and the influence of the reduction of the source domain data is alleviated. Then, a joint loss of a regularization random drop loss, a classification loss and a hierarchical feature maximum mean error is constructed to train a network model, and through the training feature constraint and the hierarchical feature maximum mean error alignment method, better domain adaptation effect is realized. The application improves the generalization capability of the model in cross-user activity recognition.

The invention discloses a cross-user wearable activity identification method based on group specific concept perception representation learning. The method comprises the steps of collecting and preprocessing multi-user sensor data; the concept offset degree is measured from a time sequence view angle and a semantic view angle, user clustering is carried out by fusing multi-view-angle measurement results, and group specific concept tags are generated; constructing a perception model comprising an activity encoder, a user encoder and a plurality of classifiers, and performing supervised learning by minimizing the joint loss of activity, user and group specific concept classification; introducing a condition discriminator to construct a representation pair of joint distribution and edge distribution, and minimizing condition mutual information of activity representation and user representation under a group specific concept through adversarial training to obtain a trained model; during application, test data are input into the trained model for activity identification. According to the method, through explicit modeling of specific concepts and decoupling of activity and user features, cross-user concept offset is eliminated, and the activity identification generalization ability of the model on new users is significantly improved.

The present invention relates to systems and methods for detecting occupant illness symptoms. Systems and methods for detecting occupant illness symptoms are disclosed herein. In embodiments, a memory is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and / or a radar source. A processor is in communication with the memory and a user interface. The processor is programmed to receive data from the one or more sources, execute a human detection model based on the received data, execute an activity recognition model that identifies illness symptoms based on the data from the one or more sources, determine a location of the identified symptoms, and execute the visualization application to display information in the user interface. The visualization application can display a background image with an overlay image that includes an indicator for each location of the identified illness symptoms. Additionally, data from the audio source, the image source, and / or the radar source can be fused.

This invention discloses a fast activity recognition method based on a multi-path parallel MLP mixer architecture. First, multi-dimensional sequence data is used as input. An embedding module encodes the multi-dimensional data to obtain a feature sequence. This feature sequence is then fed into multiple MLP mixer branches, where features are mixed along the time, channel, and frequency domain dimensions, respectively. In each branch, after passing through multiple MLP mixers, global pooling is used to aggregate the feature sequence along the time dimension into a feature vector. Finally, the feature vectors obtained from each branch are concatenated and sent to a classification head for activity recognition. Compared with existing deep learning-based activity recognition methods, this invention achieves more accurate activity recognition with fewer model parameters, less floating-point computation, and faster inference speed, meeting the requirements of high precision and high efficiency in practical applications.

To appropriately encourage actions that improve the user's health. [Solution] The health improvement device according to this embodiment includes an acquisition unit 131, an activity determination unit 133, a location identification unit 134, and an output unit 135. The acquisition unit 131 acquires information about the railway used by the user and information about the user's sleep. The activity identification unit 133 identifies activities that improve the user's health level, which indicates the degree of the user's health, based on the sleep information acquired by the acquisition unit 131. The location identification unit 134 identifies locations in the area where the user travels, as identified from the railway information, that correspond to the activities identified by the activity identification unit 133. The output unit 135 outputs the activities identified by the activity identification unit 133 and the locations identified by the location identification unit 134.

The application discloses a kind of self-correcting dead reckoning methods based on activity identification and fusion filtering, which is based on dead reckoning system, specifically comprising the following steps: first, the pedestrian data is collected by the data collection module and the data is delivered to the data processing module, then the collected pedestrian data is processed by the data processing module and the processed pedestrian data is delivered to the activity identification module, then the activity identification model is used to identify the pedestrian action based on the data processed by the activity identification module, and the result is delivered to the position estimation module, then the position of the pedestrian is estimated based on the pedestrian action by the position estimation module, and the result is delivered to the position correction module, then the position of the pedestrian is corrected based on the pedestrian position result according to the magnetic intensity sequence by the position correction module, and the result is delivered to the data output module, finally, the corrected pedestrian position result is output by the data output module, the method can effectively improve the accuracy of dead reckoning.

The invention discloses a human body activity identification method, device and system, and a storage medium, and the method comprises the steps: synchronously collecting multi-mode motion data through Wi-Fi CSI and multi-source sensors, such as an accelerometer and a gyroscope, built in a smart bracelet, and completing time synchronization, filtering noise reduction and feature extraction on an edge device; the method comprises the following steps: extracting CSI time-frequency domain features by using short-time Fourier transform (STFT), extracting CSI time-frequency domain features by using a convolutional neural network (CNN), fusing the CSI time-frequency domain features with acceleration and angular velocity data, and finally capturing a global dependency relationship of cross-modal features through a Transform model to realize high-precision recognition of complex human body actions. According to the technical scheme of the invention, the method has the advantages of comprehensive feature representation, high recognition accuracy and high adaptability, and is suitable for scenes of smart medical treatment, smart home, human-computer interaction and the like.

The application discloses a kind of federal individualized human activity identification training methods based on super network, comprising: server randomly selects multiple clients to participate in training, and broadcast embedding network parameters to these clients.Clients receive embedding network parameters, generate embedding description vector in combination with local data set, and upload to server.The server generates corresponding individualized model parameters according to the embedding description vector uploaded by each client, and then issues it to the client for local fine-tuning.After fine-tuning is completed, the client uploads the individualized model parameter update amount to the server, and the server updates the super network accordingly and generates the embedding description vector update amount and returns to the client.The client generates the update gradient of the guide embedding network in combination with the similarity consistency constraint between embedding space-individualized model parameter space and according to embedding description vector update amount.The server collects all the update gradient uploaded by the client and aggregates, and completes the update of embedding network 。

The present invention is a Multiple Intelligences Recognition and Activities System (MIRA System) that observes a student's activities inside the classroom, identifies a multiple intelligence that needs to be supplemented or is least detected for the student, and recommends activities directed to the least detected multiple intelligence(s) for each student in the class. A classroom may be in building, in a remote environment such as a home school, or outside of a building. The MIRA System comprises a digital camera or a mobile digital device and a computer program that processes captured pictures and videos from the camera, analyzes the quantity of each multiple intelligence activity for each student in the class and then recommends activities to enhance multiple intelligence skills related to the teacher's objectives.

A robotic system is configured to automatically identify surgical instruments used during a bronchoscopy procedure. The robotic system can include a video capture device, a robotic manipulator, sensors configured to detect a configuration of the robotic manipulator, and control circuitry communicatively coupled to the robotic manipulator. The control circuitry is configured to perform, using a machine learning classifier, a first analysis of a bronchoscopy video of a patient site to track a medical instrument in the bronchoscopy video. The control circuitry can then identify a set of possible instrument identifications for the medical instrument in the bronchoscopy video based on the first analysis and an identified phase of the bronchoscopy procedure. The control circuitry can then track a motion of the medical instrument in the bronchoscopy video and select an identification from the set of possible instrument identification for the medical instrument based at least on the tracked motion.

A person activity recognition system (1) comprising: an image sensor (2) arranged to collect at least one image of a personal environment and generate image data (7) from the at least one image; and a processor (3) communicatively connected to the image sensor (2) to receive image data (7), and to a memory (4) and a database (5); the memory (4) including a list of detectable objects that may be present in the personal environment and corresponding results (8) relating to an activity of the person; and the processor (3) configured to execute a plurality of machine-readable instructions stored in a non-transitory computer readable medium in the memory (4), wherein the instructions, when executed by the processor (3), cause the processor (3) to: receive the image data (7); analyse the image data (7) to detect one or more objects in the personal environment; compare the detected object(s) with the list of detectable objects; and output to the database (5) a result (8) relating to an activity of the person.

The application relates to a human activity recognition method and device, equipment and a readable storage medium, and relates to the technical field of intelligent sensing. The application comprises the following steps: performing feature extraction on original skeleton joint features of a target action to obtain skeleton joint features; performing feature extraction on original acceleration features of the target action to obtain acceleration features; performing fusion processing on the skeleton joint features and the acceleration features to obtain multi-modal fusion features; and performing recognition based on the multi-modal fusion features to obtain a recognition result corresponding to the target action. The application realizes multi-modal recognition of human activity by fusing skeleton joint data and acceleration data, so as to solve the problem of poor single-modal recognition effect and effectively improve the accuracy of human activity recognition.

The application discloses a human activity recognition method and system based on attention mask and hierarchical contrast learning, comprising: standardizing and preprocessing multi-source sensor data; calculating time step importance scores through a self-attention mechanism, guiding mask area selection, reconstructing key timing segments to learn semantic features; extracting hierarchical timing features by using a multi-scale dilated convolution network, and optimizing the model by combining instance-level and time-level contrast losses; and finally realizing low-delay real-time recognition and visual output on a mobile terminal. The application solves the problems in the prior art that random masks lack semantic guidance, single-scale contrast learning is difficult to capture multi-level timing dependencies, and the model has poor cross-dataset generalization capability, and significantly improves the accuracy and robustness of mobile human activity recognition.

The present invention is a Multiple Intelligences Recognition and Activities System (MIRA System) that observes a student's activities inside the classroom, identifies a multiple intelligence that needs to be supplemented or is least detected for the student, and recommends activities directed to the least detected multiple intelligence(s) for each student in the class. A classroom may be in building, in a remote environment such as a home school, or outside of a building. The MIRA System comprises a digital camera or a mobile digital device and a computer program that processes captured pictures and videos from the camera, analyzes the quantity of each multiple intelligence activity for each student in the class and then recommends activities to enhance multiple intelligence skills related to the teacher's objectives.

The application discloses a cross-user wearable activity recognition method based on group-specific concept-aware representation learning, which comprises the following steps: collecting multi-user sensor data and preprocessing; measuring the concept drift degree from the time sequence perspective and the semantic perspective respectively, fusing the multi-perspective measurement results to perform user clustering, and generating group-specific concept labels; constructing a perception model comprising an activity encoder, a user encoder and multiple classifiers, and performing supervised learning by minimizing the joint loss of activity, user and group-specific concept classification; introducing a conditional discriminator to construct a representation pair of joint distribution and marginal distribution, minimizing the conditional mutual information of activity representation and user representation under the group-specific concept through adversarial training, and obtaining a trained model; and inputting test data into the trained model for activity recognition. The application explicitly models the group-specific concept and decouples the activity and user features, eliminates the cross-user concept drift, and significantly improves the activity recognition generalization ability of the model on new users.

The application proposes a continuous human activity recognition method based on FMCW radar multi-domain feature fusion. Firstly, the fast Fourier transform algorithm and the multiple signal classification technology are used to process and obtain the range-Doppler image and range-angle image from the original radar data, and the radar data is mapped to the fractional domain by using the short-time fractional Fourier transform to obtain the fractional domain spectrum. Then, through the variable window length STA / LTA continuous action detection algorithm, the synchronous segmentation of three types of continuous activity sequence domain information is realized. Finally, the segmented activity samples are taken as the input of the multi-input multi-task continuous activity recognition model, the input features are extracted through the convolution network and the bidirectional long-term memory network, the multi-input representation is introduced by the CTC layer, the CTC loss of the predicted sequence and the real label is calculated, the multi-task learning is performed, and the human action recognition result is obtained. The application can accurately segment continuous human actions, accurately fuse three types of radar domain information, and realize high-precision continuous human action recognition task.

The present disclosure is directed to routine recognition for adjusting the power state of a device. Human activity recognition is performed to detect various activity states, and create a current sequence of activity states. In response to detecting a new activity state, routine comparison is performed in order to compare the current sequence to a past sequence that ended with the user starting to interact with the device. The device is preemptively turned on in response to finding a match.