Abstract: Techniques for inertial navigation aided with multi-interval pose measurements are disclosed. The techniques can include obtaining inertial measurement unit (IMU) data from an IMU, generating, based on the IMU data, a respective pose measurement vector according to each of a plurality of machine-learning models, resulting in a plurality of pose measurement vectors, wherein each of the plurality of pose measurement vectors is associated with a respective one of multiple motion classes, and determining a device pose estimate based on the IMU data and the plurality of pose measurement vectors.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for estimating shock severity during handling or delivery of assets. For example, an example of a process includes capturing, by a device, a measured acceleration for an asset associated with the device; declipping, by the device, the measured acceleration for the asset to determine a reconstructed acceleration for the asset; determining, by the device, a velocity estimate for the asset based on the reconstructed acceleration; and determining, by the device, whether the asset has experienced a severe shock based on the velocity estimate.

Abstract: In some aspects, a pose tracking device may receive usability information from a sensor system that includes a plurality of sensors based on current operating conditions associated with the plurality of sensors. The pose tracking device may select a set of sensor modalities associated with the sensor system based on the usability information. The pose tracking device may select a pose tracking model based on the set of sensor modalities selected and one or more key performance indicator (KPI) requirements related to a current context associated with a pose tracking configuration for a client application. The pose tracking device may estimate a pose associated with an object using the pose tracking model based on sensor inputs associated with the one or more sensors selected from the plurality of sensors. Numerous other aspects are described.

Abstract: In mobility scenarios, a user equipment (UE) may enter an airplane mod in which the UE has limited or no connectivity to a network. Cell selection latency after terminating the airplane mode may result in a delay in providing network or communication services. Some aspects described herein enable a reduction in latency associated with cell selection after operation in a mobility scenario. For example, a UE may determine that the UE is operating in a mobility scenario, and perform a receive-only cell selection procedure. Additionally, or alternatively, the UE may predict a destination associated with the mobility scenario and may identify a band list for performing a cell selection procedure. In this way, when the UE exits the mobility scenario, the UE reduces a time to successfully complete cell selection.

Abstract: Attention evaluation by an extended reality system, the system determining one or more regions of interest (ROI) for an image displayed to a user. The system may also receive eye tracking information indicating an area of the image that the user is looking at. The system may further generate focus statistics based on the area of the image at which the user is looking at and the one or more ROI; and output the generated focus statistics.

Abstract: System and method for determining positional and activity information of a mobile device in synchronization with the wake-up period of the mobile device to perform antenna beam management and adjusting the wake-up period based on the positional and activity information of the mobile device. A mobile device comprises: a memory; at least one sensor for detecting data: a processor communicatively coupled to the memory, the processor is configured to: synchronize the at least one sensor with a wake-up period of the mobile device; receive the data detected by the at least one sensor; determine positional information based on the received data; determine activity information based on the received data; estimate a forward position of the mobile device based on the positional information and the activity information; and perform a management of antenna beams of the mobile device based on the positional information, the activity information and the forward position.

Abstract: In an aspect, a user equipment (UE) determines a target orientation associated with the UE for a two-way satellite communication session that provides a line of sight (LOS) alignment between an antenna of the UE with a satellite antenna, determines a current orientation associated with the UE, determines difference information associated with a difference between the current orientation of the UE and the target orientation of the UE; and transmits, to a user feedback component, an instruction to provide user feedback that is based on the difference information and that comprises non-visual user feedback or visual feedback that is separate from a display screen of the UE.

Abstract: A method for energy-efficient state change detection and classification of streaming sequential data includes receiving via a first prediction model, sequential data from a sensor. The first prediction model determines a change in an activity state based on the sequential data. An indication that the activity state has changed is transmitted to a second prediction. The second prediction model determines an updated activity state based on the sequential data. The updated activity state is sent to the first prediction model, after which the second prediction enters an inactive state.

Abstract: Systems and techniques are provided for calibrating tracking devices. For example, a process can include generating, at a primary tracking device of a tracking device network, pose information of the primary tracking device; obtaining relative pose information of a secondary tracking device of the tracking device network, wherein the relative pose information of the secondary tracking device comprises a relative position and orientation between the primary tracking device and the secondary tracking device; obtaining absolute pose information of the secondary tracking device using the pose information of the primary tracking device and the relative pose information of the secondary tracking device; and transmitting, at the primary tracking device, the absolute pose information to the secondary tracking device for performing a calibration action.

Abstract: System and method for determining positional and activity information of a mobile device in synchronization with the wake-up period of the mobile device to perform antenna beam management and adjusting the wake-up period based on the positional and activity information of the mobile device. A mobile device comprises: a memory; at least one sensor for detecting data: a processor communicatively coupled to the memory, the processor is configured to: synchronize the at least one sensor with a wake-up period of the mobile device; receive the data detected by the at least one sensor; determine positional information based on the received data; determine activity information based on the received data; estimate a forward position of the mobile device based on the positional information and the activity information; and perform a management of antenna beams of the mobile device based on the positional information, the activity information and the forward position.

Abstract: Techniques and systems are provided for attention evaluation by an extended reality system. In some examples, the system determines one or more regions of interest (ROI) for an image displayed to a user. The system may also receive eye tracking information indicating an area of the image that the user is looking at. The system may further generate focus statistics based on the area of the image at which the user is looking at and the one or more ROI; and output the generated focus statistics.

Abstract: Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.

Abstract: Systems and techniques are described herein. For example, a process can include obtaining first sensor measurement data associated with a and second sensor measurement from one or more sensors. In some cases, the first measurement data can be associated with a first time and the second sensor measurement data can be associated with a second time occurring after the first time. In some aspects, the process includes determining that the first sensor measurement data and the second sensor measurement data satisfy at least one batching condition. In some examples, the process includes, based on determining that the first sensor measurement data and the second sensor measurement data satisfy the at least one batching condition, generating a sensor measurement data batch including the first sensor measurement data, the second sensor measurement data, and at least one target sensor measurement data. Ins examples the process includes outputting the sensor measurement data batch.

Abstract: Systems and techniques are described herein. For example, a process can include obtaining first sensor measurement data associated with a and second sensor measurement from one or more sensors. In some cases, the first measurement data can be associated with a first time and the second sensor measurement data can be associated with a second time occurring after the first time. In some aspects, the process includes determining that the first sensor measurement data and the second sensor measurement data satisfy at least one batching condition. In some examples, the process includes, based on determining that the first sensor measurement data and the second sensor measurement data satisfy the at least one batching condition, generating a sensor measurement data batch including the first sensor measurement data, the second sensor measurement data, and at least one target sensor measurement data. Ins examples the process includes outputting the sensor measurement data batch.

Abstract: A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to obtain electrical activity data corresponding to electrical signals from one or more electrical sources within a user's head. The one or more processors are also configured to execute the instructions to render, based on the electrical activity data, audio data to adjust a location of a sound source in a sound field during playback of the audio data.

Abstract: A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to obtain electrical activity data corresponding to electrical signals from one or more electrical sources within a user's head. The one or more processors are also configured to execute the instructions to render, based on the electrical activity data, audio data to adjust a location of a sound source in a sound field during playback of the audio data.

Abstract: A method for energy-efficient state change detection and classification of streaming sequential data includes receiving via a first prediction model, sequential data from a sensor. The first prediction model determines a change in an activity state based on the sequential data. An indication that the activity state has changed is transmitted to a second prediction. The second prediction model determines an updated activity state based on the sequential data. The updated activity state is sent to the first prediction model, after which the second prediction enters an inactive state.

Abstract: A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

Abstract: System and method for determining positional and activity information of a mobile device in synchronization with the wake-up period of the mobile device to perform antenna beam management and adjusting the wake-up period based on the positional and activity information of the mobile device. A mobile device comprises: a memory; at least one sensor for detecting data: a processor communicatively coupled to the memory, the processor is configured to: synchronize the at least one sensor with a wake-up period of the mobile device; receive the data detected by the at least one sensor; determine positional information based on the received data; determine activity information based on the received data; estimate a forward position of the mobile device based on the positional information and the activity information; and perform a management of antenna beams of the mobile device based on the positional information, the activity information and the forward position.

Abstract: Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.