A gesture-recognition (GR) device is disclosed that includes a capacitive touch sensor panel and a controller. The capacitive touch sensor panel comprises a plurality of sensing pads arranged in a cylindrical pattern inside a handle of the GR device and detects a multi-factor touch assertion at a set of sensing pads of the plurality of sensing pads. The controller transmits a driving signal to each of the plurality of sensing pads for the detection of the multi-factor touch assertion, generates an assertion signal, determines a signal sequence based on the assertion signal, and converts a current inactive state of the GR device to an active state based on a validation of the determined signal sequence corresponding to the multi-factor touch assertion and an inferred user intent.

In some examples, a machine-readable medium can store instructions executable by a processing resource to designate a first field of view of a first sensor of a head-mounted display as an active area, designate a second field of view of a second sensor of the head-mounted display as a gesture area, detect, when present, a gesture in the gesture area and cause an effect of the gesture to occur responsive to detection of the gesture.

An image processing apparatus (10) includes an image processing unit and an execution unit. The image processing unit processes a video generated by a surveillance camera (20), and thereby determines whether a person included in the video performs a first gesture. The execution unit executes first processing on a necessary condition that the first gesture is detected. As described above, a plurality of types of the first gestures exist, and the first processing is determined for each of a plurality of types of the first gestures. Then, the execution unit executes the first processing being associated with a type of the detected first gesture.

Included are: a captured image acquiring unit to acquire a captured image obtained by imaging an occupant; a temperature image acquiring unit to acquire a temperature image indicating a temperature of a surface of a body of the occupant measured in a non-contact manner; a motion detection unit to detect a motion of the occupant on the basis of the captured image; a temperature detection unit to detect a temperature of a hand of the occupant on the basis of the temperature image; and an awakening level estimating unit to estimate an awakening level of the occupant on the basis of the motion of the occupant detected by the motion detection unit and the temperature of the hand of the occupant detected by the temperature detection unit.

A method of detecting a user interaction by an electronic device according to various embodiments may include acquiring, through a camera, a first image of a real document in which at least one first area is printed, determining whether at least one first part corresponding to the at least one first area and included in the first image is changed, and performing a function of the electronic device corresponding to the at least one first part, based on determining that the at least one first part is changed.

The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

A sign language translation method performed by at least one processor includes setting a sign language translation avatar in a video call, translating speech of at least one speaker into sign language during the video call, and displaying the sign language through the sign language translation avatar during the video call.

Systems, methods, and computer-readable media herein provide for real-time manipulation and animation of 3D rigged virtual models to generate sign language translation. Source video and audio data associated with content is provided to a neural network to determine choreographic actions that may be used to modify and animate the articulation control points of a 3D model within a 3D space. The animated 3D virtual model may be presented in relation to the source content to provide sign language translation of the source content.

A control circuit configured to control a display panel includes a display driver circuit, a touch sensing circuit and a fingerprint sensing circuit. The touch sensing circuit, coupled to the display driver circuit and the fingerprint sensing circuit, is configured to detect a finger touch on the display panel, determine a position of the display panel on which the finger touch is detected, and send information associated with the position to the fingerprint sensing circuit. The fingerprint sensing circuit is configured to perform fingerprint sensing on at least one zone corresponding to the position and receive fingerprint image signals from the at least one zone correspondingly.

According to one or more embodiment, a hygiene management device includes a processor. The processor determines based on image recognition whether a plurality of predetermined procedures of a hand-washing action have been completed by a subject. The processor executes different types of processing, such as, for example, opening or closing a passage, depending on completion or incompletion of each of the plurality of predetermined procedures.