Abstract:
Methods and systems are described for determining eye position and/or for determining eye movement based on glints. An exemplary computer-implemented method involves: (a) causing a camera that is attached to a head-mounted display (HMD) to record a video of the eye; (b) while the video of the eye is being recorded, causing a plurality of light sources that are attached to the HMD and generally directed towards the eye to switch on and off according to a predetermined pattern, wherein the predetermined pattern is such that at least two of the light sources are switched on at any given time while the video of the eye is being recorded; (c) analyzing the video of the eye to detect controlled glints that correspond to the plurality of light sources; and (d) determining a measure of eye position based on the controlled glints.
Abstract:
Example methods and devices are disclosed for generating life-logs with point-of-view images. An example method may involve: receiving image-related data based on electromagnetic radiation reflected from a human eye, generating an eye reflection image based on the image-related data, generating a point-of-view image by filtering the eye reflection image, and storing the point-of-view image. The electromagnetic radiation reflected from a human eye can be captured using one or more video or still cameras associated with a suitably-configured computing device, such as a wearable computing device.
Abstract:
Systems and methods for selecting an action associated with a power state transition of a head-mounted display (HMD) in the form of eyeglasses are disclosed. A signal may be received from a sensor on a nose bridge of the eyeglasses indicating if the HMD is in use. Based on the received signal, a first power state for the HMD may be determined. Responsive to the determined first power state, an action associated with a power state transition of the HMD from an existing power state to the first power state may be selected. The action may be selected from among a plurality of actions associated with a plurality of state transitions. Also, the action may be a sequence of functions performed by the HMD including modifying an operating state of a primary processing component of the HMD and a detector of the HMD configured to image an environment.
Abstract:
Example methods and systems for determining correlated movements associated with movements caused by driving a vehicle are provided. In an example, a computer-implemented method includes identifying a threshold number of sets of correlated movements. The method further includes determining that the threshold number of sets of correlated movements is associated with movements caused by driving a vehicle. The method still further includes causing the wearable computing system to select a driving user interface for the wearable computing system.
Abstract:
Methods and devices for initiating a search of an object are disclosed. In one embodiment, a method is disclosed that includes receiving video data recorded by a camera on a wearable computing device, where the video data comprises at least a first frame and a second frame. The method further includes, based on the video data, detecting an area in the first frame that is at least partially bounded by a pointing device and, based on the video data, detecting in the second frame that the area is at least partially occluded by the pointing device. The method still further includes initiating a search on the area.
Abstract:
This document describes authentication using an interactive cord. An interactive cord includes a cable, and a fabric cover that covers the cable. The fabric cover includes one or more conductive threads woven into the fabric cover to form one or more capacitive touchpoints which are configured to enable reception of touch input that causes a change in capacitance to the one or more conductive threads. The interactive cord can be used to authenticate a user. For example, rather than using a password entered into a computing device, a touch input pattern can be provided to interactive cord that is coupled to the computing device to authenticate the user.
Abstract:
Implementations generally relate to creating groups in a social network system. In one implementation, a method includes identifying at least one person that is proximate to a target user in a social network system, determining that the target user is generating a pattern; recognizing the at least one person proximate to the target user who is generating the pattern; creating a group in the social network system, and the group includes the at least one person generating the pattern; and associating the group with the target user.
Abstract:
The present application discloses systems and methods for a virtual input device. In one example, the virtual input device includes a projector and a camera. The projector projects a pattern onto a surface. The camera captures images that can be interpreted by a processor to determine actions. The projector may be mounted on an arm of a pair of eyeglasses and the camera may be mounted on an opposite arm of the eyeglasses. A pattern for a virtual input device can be projected onto a “display hand” of a user, and the camera may be able to detect when the user uses an opposite hand to select items of the virtual input device. In another example, the camera may detect when the display hand is moving and interpret display hand movements as inputs to the virtual input device, and/or realign the projection onto the moving display hand.
Abstract:
Methods and systems are described that involve a wearable computing device or an associated device determining the orientation of a person's head relative to their body. To do so, example methods and systems may compare sensor data from the wearable computing device to corresponding sensor data from a tracking device that is expected to move in a manner that follows the wearer's body, such a mobile phone that is located in the wearable computing device's wearer's pocket.
Abstract:
Exemplary embodiments may involve analyzing reflections from an eye to help determine where the respective sources of the reflections are located. An exemplary method involves: (a) analyzing eye-image data to determine observed movement of a reflected feature on an eye surface; (b) determining an expected movement of the reflected feature on the eye surface given a value of a z-distance parameter; (c) determining a difference between the observed movement of the reflected feature on the eye surface and the expected movement of the reflected feature on the eye surface; (d) if the difference is less than a threshold, then associating the value of the z-distance parameter with a source of the reflected feature; and (e) if the difference is greater than the threshold, then: (i) making a predetermined adjustment to the value of the z-distance parameter; and (ii) repeating (a) to (d) with the adjusted value of the z-distance parameter.