According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

Techniques for updating a machine learning (ML) model are described. A device or system may receive input data corresponding to a natural or non-natural language (e.g., gesture) input. Using a first ML model, the device or system may determine the input data corresponds to a data category of a plurality of data categories. Based on the data category, the device or system may select a ML training type from among a plurality of ML training types. Using the input data, the device or system may perform the selected ML training type with respect to a runtime ML model to generate an updated ML model.

A system for detecting an anomaly in an execution of a task in mixed human-robot processes. Receiving human worker (HW) signals and robot signals. A processor to extract from the HW signals, task information, measurements relating to a state of the HW, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, to determine a classification of an anomaly or no anomaly. Update HRI model with robot operation signals, HW signals and classified anomaly, determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and classified anomaly. Output the control action of the robot to change a robot action or output the type of the anomaly alarm.

The present invention discloses a wireless augmented video system and method to monitor medical insurance billing fraud, drug or other theft and elder patient or child abuse by the caregivers. The wireless augmented system comprises; a wireless augmented monitoring devices, a cloud system monitoring center and a caregiver employing agency. The wireless wearable augmented monitoring devices further includes a smart wearable nametag apparatus and smart wearable wristband. A wireless cellular transceiver configured within the nametag apparatus to stream the augmented video stream in response to an event detected. The nametag apparatus also incorporates a memory element to buffer the augmented data prior to transmission, a SIM card to connect to any data cellular network, a Bluetooth to connect peripheral devices, a Wi-Fi component, color LCD screen for displaying current caregiver's name and photograph on the name tag, and LED power status display, a microphone, speaker and a micro USB port.

A method for (of) detecting deception in an Audio-Video response of a user, using a server, in a distributed computing architecture, characterized in that the method including: enabling an Audio-Video connection with a user device upon receiving a request from a user; obtaining, from the user device, an Audio-Video response of the user corresponding to a first set of questions that are provided to the user by the server; extracting audio signals and video signals from the Audio-Video response; detecting an activity of the user by determining a plurality of Natural Language Processing (NLP) features from the extracted audio signals by (i) performing a speech to text translation and (ii) extracting the plurality of NLP features from the translated text, and determining a plurality of speech features from the extracted audio signals by (i) splitting the extracted audio signals into a plurality of short interval audio signals and (ii) extracting the plurality of speech features from the plurality of short interval audio signals; aggregating (i) the plurality of NLP features to obtain a plurality of temporal NLP features and (ii) the plurality of speech features to obtain a plurality of temporal speech features; aggregating the plurality of temporal NLP features and the plurality of temporal speech features to obtain first temporal aggregated features; detecting a plurality of micro-expressions of the user by splitting extracted video signals into a plurality of short fixed-duration video signals, detecting a plurality of Region Of Interest (ROI) in the plurality of short fixed-duration video signals, and comparing the plurality of detected ROI with video signals annotated with micro-expression labels that are stored in a database to detect the plurality of micro-expressions of the user in the plurality of short fixed-duration video signals; tracking and determining a gesture of the user from the extracted video signals; aggregating the plurality of micro-expressions and the gesture of the user to obtain second temporal aggregated features; aggregating the first temporal aggregated features and the second temporal aggregated features to obtain final temporal aggregated features; and detecting, using a machine learning model, a deception in the Audio-Video response based on the final temporal aggregated features.