Qualcomm Incorporated

Diyan Teng

Mehul Soman

Rashmi Kulkarni

Junsheng Han

Justin McGloin

Victor Kulik

Shashank Narayanan

Nisarg Trivedi

Raehan Ahmed Syed

Mahdi Shaghaghi

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.

Calibration of interconnected tracking devices

In some aspects, a device may obtain a signal of a vibration sensor. The device may perform compressive sampling of the signal by combining each set of data points, of a plurality of sets of data points of the signal, with a compressive sampling matrix to obtain a plurality of compressive measurements of the signal. The device may determine whether the plurality of compressive measurements are indicative of a person present in a vicinity of the device. The device may cause the device to exit a low-power mode responsive to a determination that the plurality of compressive measurements are indicative of the person present in the vicinity of the device. Numerous other aspects are described.

Presence detection using compressive sampling

Systems and methods for detecting an unauthorized virtual object in a mobile device are disclosed. In an aspect, a mobile device may receive an image from a camera. The mobile device may detect a virtual object displayed on a display of a mobile device included in the image. The mobile device may receive data from an inertial measurement unit (IMU) after a movement of the mobile device. The mobile device may determine an estimated new position of the virtual object based on the data received from the IMU. The mobile device may determine an actual position of the virtual object after receiving the data from the IMU. The mobile device may determine a difference between the estimated new position and the actual position of the virtual object. The mobile device may determine whether the virtual object is the unauthorized virtual object based on the difference.

Visual content verification in extended and augmented reality

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.

Persistent two-stage activity recognition

In some aspects, a device may receive measured camera motion information associated with an image. The device may generate a shared image based on the image. The device may apply, based on the measured camera motion information, an image stabilization process to the image to generate a true image. The device may store the true image in a memory associated with the device. The device may provide the shared image and the measured camera motion information to another device. Numerous other aspects are described.

Sensor-based image verification

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.

Multi-sensor system for cardiovascular and respiratory tracking

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.

Sensor calibration

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.

Audio adjustment based on user electrical signals

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.

Weakly supervised learning for improving multimodal sensing platform

A method of controlling spatial audio rendering includes comparing a first heartbeat pattern to a second heartbeat pattern to generate a comparison result. The first heartbeat pattern is based on sensor information associated with a first sensor of a first sensor type, and the second heartbeat pattern is based on sensor information associated with a second sensor of a second sensor type. The method also includes, based on the comparison result, controlling a spatial audio rendering function associated with media playback.

Systems and methods to control spatial audio rendering

In some aspects, a user equipment (UE) determines, using an inertial measurement unit, an orientation of the UE and determines, using ambient light sensors, an ambient light condition of the UE. The UE determines, using a machine learning module and based on the orientation and the ambient light condition, a position of the UE. If the position comprises an on-body position, the UE uses the machine learning module and touch data received by a touchscreen of the UE to determine whether the position comprises an in-hand position. If the position comprises the in-hand position, the UE determines, using the machine learning module and based on the orientation and the touch data, a grip mode. If the position comprises an off-body position, the UE determines, using the machine learning module and at least one of the inertial measurement unit or the ambient light sensors, a user presence or a user absence.

Passively determining a position of a user equipment (UE)

Various aspects of the present disclosure generally relate to object detection. In some aspects, a device may include a housing; an input device adjoined to the housing, the input device configured to receive an input associated with a press of a key of a plurality of keys; one or more transmitters disposed in the housing, the one or more transmitters configured to transmit one or more signals toward the plurality of keys; one or more receivers disposed in the housing, the one or more receivers configured to receive one or more return signals corresponding to the one or more signals; and a processor configured to determine a location of the key based at least in part on the one or more return signals.

Key press detection

Methods, systems, and devices for using sensor statistics for player authentication are described. A device or system (e.g., a video game, an electronic sports (Esports) system, a gaming console, etc.) may utilize sensors to authenticate a player (e.g., a user) to an account during gameplay (e.g., in real-time during gameplay). Users may be authenticated by matching sensed gameplay attributes (e.g., indicators, reflex time, sensed patterns, eye motion, heart rate, tremors, frequency of operation, periodicity, spiky behavior, eye concentration, etc.) to the account. For example, collected sensor data may be compared to the account's history or past sensor data, or may be compared to similar ranked players' sensor data, for authentication. The device or system may further use additional data including robot (e.g., BOT) gameplay data, sensor data collected from other players of different (e.g., higher) rank, etc. to identify when collected sensor data may be associated with inauthentic gameplay.

Growing community of inventors

Santa Clara, CA, United States of America

Diyan Teng