Abstract: Embodiments are disclosed for head tracking state detection based on correlated motion of a source device and a headset communicatively coupled to the source device. In an embodiment, a method comprises: obtaining, using one or more processors of a source device, source device motion data from a source device and headset motion data from a headset; determining, using the one or more processors, correlation measures using the source device motion data and the headset motion data; updating, using the one or more processors, a motion tracking state based on the determined correlation measures; and initiating head pose tracking in accordance with the updated motion tracking state.

Abstract: During an initialization of a head pose tracker for a spatial audio system, a spatial audio ambience bed is rotated about a boresight vector to align the boresight vector with a center channel of the ambience bed. The boresight is computed using source device motion data and headset motion data. The ambience bed includes the center channel and one or more other channels. An ambience bed reference frame is aligned with a horizontal plane of a headset reference frame, such that the ambience bed is horizontally level with a user's ears. A first estimated gravity direction is fixed (made constant) in the ambience bed reference frame. During head pose tracking, the ambience bed reference frame is rolled about the boresight vector to align a second estimated gravity direction in the headset reference frame with the first estimated gravity direction fixed in the ambience bed reference frame.

Abstract: In an embodiment, a method comprises: estimating a first gravity direction in a source device reference frame for a source device; estimating a second gravity direction in a headset reference frame for a headset; estimating a rotation transform from the headset frame into a face reference frame using the first and second estimated gravity directions, a rotation transform from a camera reference frame to the source device reference frame, and a rotation transform from the face reference frame to the camera reference frame; estimating a relative position and attitude using source device motion data, headset motion data and the rotation transform from the headset frame to the face reference frame; using the relative position and attitude to estimate a head pose; and using the estimated head pose to render spatial audio for playback on the headset.

Abstract: Embodiments are disclosed for head motion prediction for spatial audio applications. In an embodiment, a method comprises: obtaining motion data from a source device and a headset; obtaining transmission delays from a wireless stack of the source; estimating relative motion from the relative source device and headset motion data; calculating a first derivative of the relative motion data; forward predicting the estimated relative motion over the time delays using the first derivative and second derivative of relative motion; determining, using a head tracker, a head pose of the user based on the forward predicted relative motion data; and rendering, using the head pose, spatial audio for playback on the headset.

Abstract: Embodiments are disclosed for user posture change detection for triggering re-centering of spatial audio. In an embodiment, a method comprises: obtaining source device motion data from a source device and headset motion data from a headset worn by a user; estimating a gravity vector from one of the source device or headset motion data; splitting the source device and headset motion data into vertical and horizontal planes, the vertical plane in the direction of the estimated gravity vector and the horizontal plane perpendicular to the estimated gravity vector; calculating similarity measures based on the source device motion data and headset motion data in the vertical and horizontal planes over a time window; detecting a posture change event based on the calculated similarity measures; and resetting a head tracker error after the detected user posture change event.

Abstract: An electronic device includes a touch-sensitive surface, a display, and a camera sensor. The device displays a message region for displaying a message conversation and receives a request to add media to the message conversation. Responsive to receiving the request, the device displays a media selection interface concurrently with at least a portion of the message conversation. The media selection interface includes a plurality of affordances for selecting media for addition to the message conversation, the plurality of affordances includes a live preview affordance, at least a subset of the plurality of affordances includes thumbnail representations of media available for adding to the message conversation, and the live preview affordance is associated with a live camera preview. Responsive to detecting selection of the live preview affordance, the device captures a new image based on the live camera preview and selects the new image for addition to the message conversation.

Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: In an example method, a mobile device connects a voice call for a user. The voice call causes one or more radio frequency transmitters of the mobile device to transmit radio waves at a first power level. Motion data describing movement of the mobile device is obtained, and the orientation of the mobile device is determined based on the motion data. A determination whether the mobile device is on the user's body or on an inanimate object is made based on the orientation of the mobile device over the period of time. The transmit power level is adjusted based on the determination.

Abstract: A system and method for improving the accuracy of a user device when generating map/navigation information for display to a user, comprising: obtaining compass heading from a magnetometer of the user device located within a vehicle; adjusting the compass heading based on a mount angle of the user device within the vehicle; obtaining location data from a location sensor of the user device; determining if a course of the vehicle can be reliably determined from the location data; if the course of the vehicle cannot be reliably determined from the location data, determining the orientation of the vehicle using the compass heading but not the course; if the course of the vehicle can be reliably determined from the location data, calculating a course of the vehicle based on the location data and determining the orientation of the vehicle using the course; generating, by a processor, first map/navigation information using the orientation of the vehicle; and displaying, by a processor, the first map/navigation inform

Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: In one aspect, the present disclosure relates to a method, including determining, by a wearable device, receiving, by a wearable device, motion information from a motion sensor of the wearable device, determining, by the wearable device using the motion information, that a vehicle is turning, and determining, by the wearable device using the motion information when the vehicle is turning, that a user of the wearable device is controlling the vehicle.

Abstract: Applications may be tagged with location data when they are used. Mobile device may anonymously submit application usage data. Aggregated application usage data from many mobile devices may be analyzed to determine applications that are particularly relevant to a given location (i.e., exhibiting a high degree of localization). Analysis may include determining the application usage intensity, whether hotspots exist or not at a given location, the spatial entropy of a particular application, the device populations in a particular area, etc. Based on the localized application analysis, applications may be ranked according to local relevance, and, based on this ranking, application recommendations may be provided to a user.

Abstract: Systems, methods, and computer-readable media for managing or classifying movement states of an electronic device are provided that may utilize communications circuitry data from one or more communications circuitries when determining a current or future movement state of an electronic device.

Abstract: Methods and systems for improving signal reception and call quality by detecting a user's handedness of holding a mobile device during a call are described. Motion data of the mobile device can be collected by one or more motion sensor. A screen tilt angle can be determined and compared with a threshold tilt angle. A screen swivel angle can be determined and compared with a threshold swivel angle. A call handedness can be determined based on the screen swivel angle. One or more parameters of an antenna can be adjusted based on the call handedness.

Abstract: A wearable computing device can detect device-raising gestures. For example, onboard motion sensors of the device can detect movement of the device in real time and infer information about the spatial orientation of the device. Based on analysis of signals from the motion sensors, the device can detect a raise gesture, which can be a motion pattern consistent with the user moving the device's display into his line of sight. In response to detecting a raise gesture, the device can activate its display and/or other components. Detection of a raise gesture can occur in stages, and activation of different components can occur at different stages.

Abstract: The embodiments described relate to techniques and systems for utilizing a portable electronic device to monitor, process, present and manage data captured by a series of sensors and location awareness technologies to provide a context aware map and navigation application. The context aware map application offers a user interface including visual and audio input and output, and provides several map modes that can change based upon context determined by data captured by a series of sensors and location awareness technologies.

Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: A system and method for improving the accuracy of a user device when generating map/navigation information for display to a user, comprising: obtaining compass heading from a magnetometer of the user device located within a vehicle; adjusting the compass heading based on a mount angle of the user device within the vehicle; obtaining location data from a location sensor of the user device; determining if a course of the vehicle can be reliably determined from the location data; if the course of the vehicle cannot be reliably determined from the location data, determining the orientation of the vehicle using the compass heading but not the course; if the course of the vehicle can be reliably determined from the location data, calculating a course of the vehicle based on the location data and determining the orientation of the vehicle using the course; generating, by a processor, first map/navigation information using the orientation of the vehicle; and displaying, by a processor, the first map/navigation inform

Abstract: Methods and systems for improving signal reception and call quality by detecting a user's handedness of holding a mobile device during a call are described. Motion data of the mobile device can be collected by one or more motion sensor. A screen tilt angle can be determined and compared with a threshold tilt angle. A screen swivel angle can be determined and compared with a threshold swivel angle. A call handedness can be determined based on the screen swivel angle. One or more parameters of an antenna can be adjusted based on the call handedness.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.