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: Depth images acquired during a scan of one or more portions of a user's body may be used to determine a full-body three-dimensional (3D) representation of the user. Depth imaging data may be used to determine the full-body shape and contours of the user when rotating in front of a user device. Machine learning methods for pose detection may be used to determine the portion of the user's body being scanned, as well as the position and pose of the user's body. A point cloud alignment process that leverages knowledge of user behavior during the scan is used to determine parameters for a full-body representation of a user, which may be based on a plurality of partial scans of the user's body. A full-body representation of a user may be output and/or displayed along with accurate measurements determined for various portions of the user's body.

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: 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: 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: 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: 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: An example method includes obtaining a plurality of data items. Each data item includes an indication of a particular location, an indication that a wireless signal from a first access point was observed at that location, and an indication of a time at which the wireless signal from the first access point was observed at that location. The method also includes determining a locational stability of the first access point based on the data items. Determining the locational stability of the first access point includes clustering the plurality of data items into one or more clusters based on the locations indicated in the plurality of data items, determining whether the N most recent data items are associated with a common cluster, and determining whether a time span between the N most recent data items exceeds a threshold period of time.

Abstract: An example method includes obtaining a plurality of data items. Each data item includes an indication of a particular location, an indication that a wireless signal from a first access point was observed at that location, and an indication of a time at which the wireless signal from the first access point was observed at that location. The method also includes determining a locational stability of the first access point based on the data items. Determining the locational stability of the first access point includes clustering the plurality of data items into one or more clusters based on the locations indicated in the plurality of data items, determining whether the N most recent data items are associated with a common cluster, and determining whether a time span between the N most recent data items exceeds a threshold period of time.