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: 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: Embodiments are disclosed for adaptive audio centering for head tracking in spatial audio applications. In an embodiment, a method comprises: obtaining first motion data from an auxiliary device communicatively coupled to a source device, the source device configured to provide spatial audio content and the auxiliary device configured to playback the spatial audio content; obtaining second motion data from one or more motion sensors of the source device; determining whether the source device and auxiliary device are in a period of mutual quiescence based on the first and second motion data; in accordance with determining that the source device and the auxiliary device are in a period of mutual quiescence, re-centering the spatial audio in a three-dimensional virtual auditory space; and rendering the 3D virtual auditory space for playback on the auxiliary device.

Abstract: Embodiments are disclosed for PDR for different device placements. In some embodiments, a method comprises: obtaining motion data from a motion sensor of a mobile device; determining pedestrian or non-pedestrian class based on the device motion data; determining a placement of the device based on the device motion data; estimating a first direction of travel based on multiple direction of travel sources and the device placement; estimating a first velocity based on the pedestrian or non-pedestrian classification, the first estimate of direction of travel and a first estimate of speed; estimating a second velocity based on a kinematic model and the device motion data; selecting the first estimated velocity or the second estimated velocity based on selection logic; and determining a relative position of the device based on the selected estimated velocity.

Abstract: In some implementations, responsive to a trigger signal at an associated first time, a mobile device generating a first location value using a first ranging session with one or more other devices. The technique may include storing the first location value in a memory. The technique may include tracking, using a motion sensor of the mobile device, motion of the mobile device to determine a present location relative to the first location value. Further, the technique may include determining that a present location for the mobile device has changed by a predetermined threshold amount from the first location value since the associated first time. Responsive to the present location for the mobile device having changed by more than the predetermined threshold amount since the associated first time, the technique may include, generating a second location value using a second ranging session with the one or more other devices.

Abstract: A mobile device may determine a set of range values between the first mobile device and the second mobile device based on a difference between the first time and the second time for ranging wireless signals. The mobile device can determine first odometry information using a first sensor on the first mobile device, the first odometry information indicating a first motion of the first mobile device during a time period. The mobile device can receive, via a data channel, second odometry information determined from second measurements captured using a second sensor on the second mobile device. The mobile device can solve an angle between a first reference frame for the first device and a second reference frame for the second device using the set of range values, and the first and second odometry information to display a directional arrow pointing to a current position of the second mobile device.

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: 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: Embodiments are disclosed for head dimension estimation for spatial audio applications. In an embodiment, a method comprises: obtaining, using one or more processors of an audio headset worn on a user's head, acceleration samples and rotation rate samples over a specified time window while the user rotates their head, the acceleration samples and rotation rate samples measured using motion sensors in the headset; determining a function that relates the acceleration samples to the rotation rate samples; comparing the function to a plurality of reference functions, where each reference function corresponds to a different head dimension in a nominal range of head dimensions; and estimating a dimension of the user's head based on the comparing.

Abstract: The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.

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: 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: The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.

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 method and system for sanitizing at least one surface in an area to be sanitized are provided. At least one illumination module is positioned to radiate the at least one surface with sanitizing electromagnetic radiation (SER). A dosimeter is positioned to measure a level of SER emitted by the at least one illumination module or received by the at least one surface. A control module is in communication with the dosimeter to receive the level of SER emitted by the at least one illumination module or received by the at least one surface, determine an appropriate level of SER to be provided to the at least one surface at least partially based on the level of SER emitted or received by the at least one surface, and control the at least one illumination module to radiate the at least one surface with the determined appropriate level of SER.

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: Ear buds may have optical proximity sensors and accelerometers. Control circuitry may analyze output from the optical proximity sensors and the accelerometers to identify a current operational state for the ear buds. The control circuitry may also analyze the accelerometer output to identify tap input such as double taps made by a user on ear bud housings. Samples in the accelerometer output may be analyzed to determine whether the samples associated with a tap have been clipped. If the samples have been clipped, a curve may be fit to the samples. Optical sensor data may be analyzed in conjunction with potential tap input data from the accelerometer. If the optical sensor data is ordered, a tap input may be confirmed. If the optical sensor data is disordered, the control circuitry can conclude that accelerometer data corresponds to false tap input associated with unintentional contact with the housing.

Abstract: One or more acoustic parameters of a current acoustic environment of a user may be determined based on sensor signals captured by one or more sensors of the device. One or more preset acoustic parameters may be determined based on the one or more acoustic parameters of the current acoustic environment of the user and an acoustic environment of an audio file comprising audio signals that is determined based on the audio signals of the audio file or metadata of the audio file. The audio signals may be spatially rendered by applying spatial filters that include the one or more preset acoustic parameters to the audio signals, resulting in binaural audio signals. The binaural audio signals may be used to drive speakers of a headset. Other aspects are described and claimed.

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.