Abstract: Systems and techniques are provided for sensing and device tracking. For example, a process can include obtaining first sensor data from a plurality of sensors configured with a first sensor operation mode and included in a tracker device of a plurality of tracker devices. Neighbor context information can be determined for the tracker device based on passive sensing data included in the first sensor data, and can be indicative of a placement of the tracker device relative to one or more additional tracker devices included in the plurality of tracker devices. An updated sensor operation mode can be determined for the tracker device in response to one or more changes in the neighbor context information, the updated sensor operation mode different from the first sensor operation mode. Second sensor data can be obtained from the plurality of sensors based on configuring the tracker device with the updated sensor operation mode.

Abstract: Certain aspects of the present disclosure generally relate to an apparatus with a virtual keyboard being configured to compensate, or at least adjust, for motion of the apparatus during receipt of a swipe gesture. Other aspects of the present disclosure generally relate to a method for receiving data in an apparatus with a touchscreen. An exemplary data reception method generally comprises receiving a swipe gesture input sequence on a virtual keyboard of the touchscreen, wherein the apparatus with the touchscreen is undergoing motion during at least part of the swipe gesture input sequence; sensing at least one parameter indicative of the motion using at least one inertial sensor; and determining a character sequence based on the swipe gesture input sequence and the at least one parameter indicative of the motion.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems, and devices for game matchmaking are described. The methods, systems, and devices for game matchmaking may include determining a ranking of a first user of a set of users in a game environment, monitoring, via a sensor, a performance attribute of the first user while the first user plays a game in the game environment, modifying the ranking of the first user in the game environment based on the monitored performance attribute of the first user, and, in some examples, matching the first user with a second user of the set of users based on the modified ranking of the first user.

Abstract: Disclosed embodiments pertain to cardiovascular parameter (e.g. heart rate) measurements when motion is present. Biometric sensor signal measurements may be obtained based on cardiovascular parameters of a user; and motion sensor signal measurements may be obtained based on user motion. An activity type may be determined based on the motion sensor signals. For example, when non-motion related frequencies in a frequency domain representation of the biometric sensor signal are obscured by user motion, an activity type may be determined based on the motion sensor signals. Further, based on the activity type, for each cardiovascular parameter (e.g. heart rate), a corresponding likely cardiovascular parameter value (e.g. a likely heart rate) may be determined. A corresponding fundamental frequency associated with the biometric sensor signal may then be determined for each cardiovascular parameter based on the motion sensor signal measurements and the corresponding likely cardiovascular parameter value.

Abstract: Disclosed embodiments pertain to cardiovascular parameter (e.g. heart rate) measurements when motion is present. Biometric sensor signal measurements may be obtained based on cardiovascular parameters of a user; and motion sensor signal measurements may be obtained based on user motion. An activity type may be determined based on the motion sensor signals. For example, when non-motion related frequencies in a frequency domain representation of the biometric sensor signal are obscured by user motion, an activity type may be determined based on the motion sensor signals. Further, based on the activity type, for each cardiovascular parameter (e.g. heart rate), a corresponding likely cardiovascular parameter value (e.g. a likely heart rate) may be determined. A corresponding fundamental frequency associated with the biometric sensor signal may then be determined for each cardiovascular parameter based on the motion sensor signal measurements and the corresponding likely cardiovascular parameter value.

Abstract: Methods, systems, computer-readable media, and apparatuses for a smart device are presented. In some implementations, a system comprises a main device including a processor, and a band including one or more sensors, memory, and a battery. The band may be configured to communicatively and mechanically connect to the main device, and to store data received from the one or more sensors into the memory. Upon being connected to the band, the processor may be configured to obtain the stored data from the memory of the band, and process the stored data obtained from the memory of the band.