A method of gesture recognition is provided comprising: receiving data from each of a first sensor, a second sensor, a third sensor and a fourth sensor in a wearable glove device, data from the first sensor relating to movement of a thumb, data from the second sensor relating to movement of a first finger, data from the third sensor relating to movement of a middle finger and data from the fourth sensor relating to movement of a hand; forming an input sequence from the data; comparing the input sequence to each of a plurality of reference sequences by applying dynamic time warping to determine a similarity measure, wherein each reference sequence corresponds to an item in a stored vocabulary; and determining an output item from the stored vocabulary using the similarity measure.

Apparatuses, systems and methods are described herein for separately collecting and storing form contents. Different information may be collected from a user on a form. Based on determining that portions of the form request a first type of information, an extended reality (XR) environment may be provided to the user. The user may provide the first type information via an input method in the XR environment. The user may provide a second type of information using a different input method outside of the XR environment. The first type of information may be processed and stored in a different database as compared to the second type of information. The database storing the first type of information may have a higher security standard than the database storing second type of information.

Methods, apparatus and systems for recognizing sign language movements using multiple input and output modalities. One example method includes capturing a movement associated with the sign language using a set of visual sensing devices, the set of visual sensing devices comprising multiple apertures oriented with respect to the subject to receive optical signals corresponding to the movement from multiple angles, generating digital information corresponding to the movement based on the optical signals from the multiple angles, collecting depth information corresponding to the movement in one or more planes perpendicular to an image plane captured by the set of visual sensing devices, producing a reduced set of digital information by removing at least some of the digital information based on the depth information, generating a composite digital representation by aligning at least a portion of the reduced set of digital information, and recognizing the movement based on the composite digital representation.

In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.

The present disclosure relates to methods and systems for determining a state indicating whether a seat belt of a vehicle is used. A computer implemented method for determining a state indicating whether a seat belt of a vehicle is used comprises: acquiring at least one image of a portion of an interior of the vehicle; determining whether the at least one image comprises a buckle receiver; if it is determined that the at least one image comprises the buckle receiver, determining the state based on the image, and otherwise performing: extracting, from the acquired image, information related to a user of the seat belt and/or information related to a buckle of the seat belt; determining a probability of a change of the state based on the extracted information, and updating the state based on the determined probability.

A control system and method for controlling a vehicle's functions using an in-vehicle gesture input, and more particularly, a system for receiving an occupant's gesture and controlling the execution of vehicle functions. The control system using an in-vehicle gesture input includes an input unit configured to receive a user's gesture, a memory configured to store a control program using an in-vehicle gesture input therein, and a processor configured to execute the control program. The processor transmits a command for executing a function corresponding to a gesture according to a usage pattern.

Example implementations described herein involve systems and methods for a mobile application device to playback and record augmented reality (AR) overlays indicating gestures to be made to a recorded device screen. A device screen is recorded by a camera of the mobile device, wherein a mask is overlaid on a user hand interacting with the device screen. Interactions made to the device screen are detected based on the mask, and AR overlays are generated corresponding to the reactions.

A method and system for generating training data for training a gesture detection machine-learning (ML) model includes receiving a request to generate training data for the gesture detection model, the training data being associated with a target gesture, retrieving data associated with an original gesture, the original gesture being a gesture made using a body part, retrieving skeleton data associated with the target gesture, the skeleton data displaying a skeleton representative of the body part and the skeleton displaying the target gesture, aligning a location of the body part in the data with a location of the skeleton in the skeleton data, providing the aligned data and the skeleton data to an ML model for generating a target data that displays the target gesture, receiving the target data as an output from the ML model, the target data preserving a visual feature of the data and displaying the target gesture, and providing the target data to the gesture detection ML model.

An autonomous vehicle (an AV, or manual vehicle in an autonomous or semi-autonomous mode) includes the ability to sense a command from a source external to the vehicle and modify the behavior of the vehicle in accordance with the command. For example, the vehicle may visualize a police officer or other person associated with traffic control and interpret gestures made by the person causing the vehicle to stop, slow down, pull over, change lanes, back up or take a different route due to unplanned traffic patterns such as accidents, harsh weather, road closings or other situations. The system and method may also be used for non-emergency purposes, including external guidance for load pick-up/placement, hailing a vehicle used as a cab, and so forth. The command may further be spoken or may include a radio frequency (RF) light or other energy component.

A motion analysis device, a motion analysis method and a motion analysis program that make it possible for an operator to improve his or her motions more smoothly are provided. The motion analysis device includes an acquisition part that relates to motions of a plurality of parts of an operator and acquires time-series data relating to an operation performed by an operator and; an analysis part analyzing the time-series data and generating motion data indicating a type of elemental motion and an execution time of the elemental motion from start to end thereof; an evaluation part evaluating the elemental motion performed by the plurality of parts based on an execution timing of the elemental motion; and a display control part performing control to differentiates periods corresponding to different elemental motions and display the evaluation together with the motion data on a display part.