Abstract: In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

Abstract: A system and method for a master platform includes receiving first pose data associated with an image sensor of a first device, and a first semantic map generated by the first device, the first semantic map including a simplified object representation in a coordinate space of the first device. The master platform also receives second pose data associated with an image sensor of a second device, and a second semantic map generated by the second device, the second semantic map including a simplified object representation in a coordinate space of the second device. A shared simplified object representation common to the first and semantic maps is identified. The master platform further combines the first semantic and second semantic maps based on the first and second pose data. The first pose data, first semantic map, second pose data, and second semantic map are associated with a common time interval.

Abstract: An untethered apparatus for performing inside-out device tracking based on visual-inertial simultaneous location and mapping (SLAM) includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, an inertial measurement unit (IMU) sensor configured to collect IMU data associated with motion of the apparatus at a predetermined interval, a processor and a memory. The memory contains instructions, which when executed by the processor, cause the apparatus to accumulate DVS sensor output over a sliding time window, the sliding time window including the predetermined interval, apply a motion correction to the accumulated DVS sensor output, the motion correction based on the IMU data collected over the predetermined interval, generate an event-frame histogram of DVS sensor events based on the motion correction, and provide the event-frame histogram of the DVS sensor events and the IMU data to a visual inertial SLAM pipeline.

Abstract: An apparatus includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, and a complementary metal-oxide-semiconductor (CMOS) image sensor configured to output frames of image data. The apparatus further includes a hybrid feature handler configured to receive, as an input, one or more of a DVS output or a CMOS image sensor output, and provide tracked features to a visual-inertial simultaneous location and mapping (SLAM) pipeline performing inside-out device tracking, and a sensor scheduler configured to switch off the CMOS image sensor based on a current value of one or more CMOS control factors.

Abstract: An untethered apparatus for performing inside-out device tracking based on visual-inertial simultaneous location and mapping (SLAM) includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, an inertial measurement unit (IMU) sensor configured to collect IMU data associated with motion of the apparatus at a predetermined interval, a processor and a memory. The memory contains instructions, which when executed by the processor, cause the apparatus to accumulate DVS sensor output over a sliding time window, the sliding time window including the predetermined interval, apply a motion correction to the accumulated DVS sensor output, the motion correction based on the IMU data collected over the predetermined interval, generate an event-frame histogram of DVS sensor events based on the motion correction, and provide the event-frame histogram of the DVS sensor events and the IMU data to a visual inertial SLAM pipeline.

Abstract: A system and method for a master platform includes receiving first pose data associated with an image sensor of a first device, and a first semantic map generated by the first device, the first semantic map including a simplified object representation in a coordinate space of the first device. The master platform also receives second pose data associated with an image sensor of a second device, and a second semantic map generated by the second device, the second semantic map including a simplified object representation in a coordinate space of the second device. A shared simplified object representation common to the first and semantic maps is identified. The master platform further combines the first semantic and second semantic maps based on the first and second pose data. The first pose data, first semantic map, second pose data, and second semantic map are associated with a common time interval.

Abstract: A system for determining and tracking camera pose includes a dynamic vision sensor (DVS) configured to generate a current DVS image, an inertial measurement unit (IMU) configured to generate inertial data, and a memory. The memory is configured to store a 3-dimensional (3D) map of a known 3D environment. The system may also include a processor coupled to the memory. The processor is configured to initiate operations including determining a current camera pose for the DVS based on the current DVS image, the inertial data, the 3D map, and a prior camera pose.

Abstract: An apparatus includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, a CMOS image sensor configured to output frames of image data, an inertial measurement unit (IMU), a processor and a memory. The memory contains instructions, which when executed by the processor, cause the apparatus to generate a semantic segmentation of a time-stamped frame, which is based on one or more of an output of the CMOS image sensor, or a synthesized event frame based on an output from the DVS and an output from the IMU over a time interval. The semantic segmentation includes a semantic label associated with a region of the time-stamped frame. When executed, the instructions further cause the apparatus to determine, based on the semantic segmentation, a simplified object representation in a coordinate space, and update a stable semantic map based on the simplified object representation.

Abstract: A system and method enable an electronic device to perform gaze tracking operations. The electronic device includes one or more cameras and at least one processor. The one or more cameras include a Dynamic Vision Sensor (DVS) camera. The one or more cameras are configured to capture features of an eye of a wearer of the electronic device The processor is configured to receive, from the one or more cameras, an image or pixel stream associated with the eye, determine an initial pose of the eye based on the image or pixel stream, receive DVS pixel data from the DVS camera, track one or more changes to the initial pose of the eye based on the DVS pixel data, and based on the one or more changes to the initial pose of the eye, determine a gaze of the wearer.

Abstract: A system and method enable an electronic device to perform gaze tracking operations. The electronic device includes one or more cameras and at least one processor. The one or more cameras include a Dynamic Vision Sensor (DVS) camera. The one or more cameras are configured to capture features of an eye of a wearer of the electronic device The processor is configured to receive, from the one or more cameras, an image or pixel stream associated with the eye, determine an initial pose of the eye based on the image or pixel stream, receive DVS pixel data from the DVS camera, track one or more changes to the initial pose of the eye based on the DVS pixel data, and based on the one or more changes to the initial pose of the eye, determine a gaze of the wearer.

Abstract: A method of camera pose and plane estimation may include detecting a marker within a 3-dimensional (3D) environment by detecting, using a Dynamic Vision Sensor (DVS), a first plurality of light sources arranged in a known shape and blinking at a first frequency, wherein the known shape corresponds to the marker, determining an orientation and an identity of the marker based upon detecting, using the DVS, a second plurality of light sources corresponding to the marker and blinking at a second frequency different from the first frequency. A camera pose for the DVS may be determined based upon the known shape, the orientation, and the identity of the marker using the processor.

Abstract: An apparatus includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, and a complementary metal-oxide-semiconductor (CMOS) image sensor configured to output frames of image data. The apparatus further includes a hybrid feature handler configured to receive, as an input, one or more of a DVS output or a CMOS image sensor output, and provide tracked features to a visual-inertial simultaneous location and mapping (SLAM) pipeline performing inside-out device tracking, and a sensor scheduler configured to switch off the CMOS image sensor based on a current value of one or more CMOS control factors.

Abstract: An apparatus includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, a CMOS image sensor configured to output frames of image data, an inertial measurement unit (IMU), a processor and a memory. The memory contains instructions, which when executed by the processor, cause the apparatus to generate a semantic segmentation of a time-stamped frame, which is based on one or more of an output of the CMOS image sensor, or a synthesized event frame based on an output from the DVS and an output from the IMU over a time interval. The semantic segmentation includes a semantic label associated with a region of the time-stamped frame. When executed, the instructions further cause the apparatus to determine, based on the semantic segmentation, a simplified object representation in a coordinate space, and update a stable semantic map based on the simplified object representation.

Abstract: In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

Abstract: A system for determining and tracking camera pose includes a dynamic vision sensor (DVS) configured to generate a current DVS image, an inertial measurement unit (IMU) configured to generate inertial data, and a memory. The memory is configured to store a 3-dimensional (3D) map of a known 3D environment. The system may also include a processor coupled to the memory. The processor is configured to initiate operations including determining a current camera pose for the DVS based on the current DVS image, the inertial data, the 3D map, and a prior camera pose.

Abstract: A method of camera pose and plane estimation may include detecting a marker within a 3-dimensional (3D) environment by detecting, using a Dynamic Vision Sensor (DVS), a first plurality of light sources arranged in a known shape and blinking at a first frequency, wherein the known shape corresponds to the marker, determining an orientation and an identity of the marker based upon detecting, using the DVS, a second plurality of light sources corresponding to the marker and blinking at a second frequency different from the first frequency. A camera pose for the DVS may be determined based upon the known shape, the orientation, and the identity of the marker using the processor.