Hewlett-packard Development Company, L.P.

Joseph Nouri

Robert Paul Martin

Nathan Barr Nuber

Mark Lessman

Eric Bokides

Kevin Smathers

Owen T Richard

Peter Christian Peterson

Kaden Strand

Louis M Gaiot

Tsung-Nung Huang

Paul Martin

Nathan Nuber

A computing device receives a notification from a tracking device that the tracking device has entered a gripped state in which a user has securely gripped the tracking device with a hand of the user. The computing device responsively receives orientation data from the tracking device indicative of an orientation of the tracking device in a tracking device space. The computing device determines an orientation of the hand of the user in a hand tracking space from captured image data of the hand of the user and the tracking device. The computing device generates a transformation matrix to anchor the tracking device space to the hand tracking space. The tracking device resultantly enters an anchored state.

Anchoring tracking device space to hand tracking space

As sensor readings are received from a hardware sensor, each sensor reading is stored within a buffer along with a system timestamp of a system clock indicating when the sensor reading was received. Each sensor reading includes a sensor timestamp of a sensor clock indicating when the sensor reading occurred at the hardware sensor. Responsive to receipt of each sensor reading, a synthetic timestamp compensating for skew between the system clock and the sensor clock is generated and applied to the sensor reading. The synthetic timestamp is generated based on an average skew of the sensor readings within the buffer and an initial system timestamp indicating when a first sensor reading was received.

Synthetic timestamp compensating for skew between system clock and sensor clock

A computing device is configured to downsample an image that is displayable at a head-mountable display (HMD) to an image pixel representative of the image. The computing device computes a luminance of the image pixel from a color value of the image pixel to determine image brightness. The computing device is configured to determine the foveal region of the image from eye-tracking information of the user of the HMD. The computing device dowsamples the foveal region of the image to a foveal pixel representative of the foveal region. The computing device is configured to compute a luminance of the foveal pixel from a color value of the foveal pixel to determine foveal region brightness.

Head-mountable display (HMD) image and foveal region brightness computation

In an example implementation, a head-mounted display (HMD) device includes a display, a variable lens receptacle to receive an interchangeable lens, a sensor to retrieve lens information from the lens, and a controller to obtain images that are predistorted according to the lens information, wherein the controller is to display the predistorted images on the display.

Head-mounted displays

Computer-implemented methods, systems, apparatus, and computer-readable media (transitory or non-transitory) are described herein for detecting eye tracking calibration errors. In various examples, respective poses of first and second eyes of a wearer of a head-mounted display ('HMD') may be determined. A portion of a first image rendered on a first display of the HMD may be selected based on the pose of the first eye of the wearer. The selected portion of the first image may be matched to a portion of a second image rendered on a second display of the HMD. An eye tracking calibration error may be determined based on the pose of the second eye of the wearer and a location of the portion of the second image.

Detecting eye tracking calibration errors

Structured light is projected onto an object, and an image of the object as illuminated by the projected structured light is captured. Reference object points are identified within the captured image, and the structured light projected onto the object is modified based on the identified reference object points. An additional image of the object as illuminated by the modified projected structured light is captured, and additional reference object points are identified within the captured additional image.

Modification of projected structured light based on identified points within captured image

In some examples, a facial gasket device includes a plurality of physiological sensors to detect physiological signals, and a signal processor to receive output signals based on measurements of the plurality of physiological sensors, the signal processor programmable to selectively combine a collection of the output signals to detect a target physiological activity of the user.

Signal combination of physiological sensor signals

In various examples, a plurality of reference points may be generated by processing an image of an eye of a user from a vision sensor. The plurality of reference points may correspond to, for example, a boundary between a sclera of the eye and an eyelid of the user, a boundary between a pupil and a cornea, etc. Based on the reference points corresponding to the boundary between the sclera and the eyelid, a measure of the eye of the user may be detected.

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Fort Collins, CO, United States of America

Joseph Nouri