Abstract: A wearable computing device can detect device-raising gestures. For example, onboard motion sensors of the device can detect movement of the device in real time and infer information about the spatial orientation of the device. Based on analysis of signals from the motion sensors, the device can detect a raise gesture, which can be a motion pattern consistent with the user moving the device's display into his line of sight. In response to detecting a raise gesture, the device can activate its display and/or other components. Detection of a raise gesture can occur in stages, and activation of different components can occur at different stages.

Abstract: A wearable computing device can detect device-raising gestures. For example, onboard motion sensors of the device can detect movement of the device in real time and infer information about the spatial orientation of the device. Based on analysis of signals from the motion sensors, the device can detect a raise gesture, which can be a motion pattern consistent with the user moving the device's display into his line of sight. In response to detecting a raise gesture, the device can activate its display and/or other components. Detection of a raise gesture can occur in stages, and activation of different components can occur at different stages.

Abstract: A wearable computing device can detect device-raising gestures. For example, onboard motion sensors of the device can detect movement of the device in real time and infer information about the spatial orientation of the device. Based on analysis of signals from the motion sensors, the device can detect a raise gesture, which can be a motion pattern consistent with the user moving the device's display into his line of sight. In response to detecting a raise gesture, the device can activate its display and/or other components. Detection of a raise gesture can occur in stages, and activation of different components can occur at different stages.

Abstract: A real-time calibration system and method for a mobile device having an onboard magnetometer uses an estimator to estimate magnetometer calibration parameters and a magnetic field external to the mobile device (e.g., the earth magnetic field). The calibration parameters can be used to calibrate uncalibrated magnetometer readings output from the onboard magnetometer. The external magnetic field can be modeled as a weighted combination of a past estimate of the external magnetic field and the asymptotic mean of that magnetic field, perturbed by a random noise (e.g., Gaussian random noise). The weight can be adjusted based on a measure of the statistical uncertainty of the estimated calibration parameters and the estimated external magnetic field. The asymptotic mean of the external magnetic field can be modeled as a time average of the estimated external magnetic field.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Abstract: A device provides user interfaces for capturing and sending media, such as audio, video, or images, from within a message application. The device detects a movement of the device and in response, plays or records an audio message. The device detects a movement of the device and in response, sends a recorded audio message. The device removes messages from a conversation based on expiration criteria. The device shares a location with one or more message participants in a conversation.

Abstract: A device provides user interfaces for capturing and sending media, such as audio, video, or images, from within a message application. The device detects a movement of the device and in response, plays or records an audio message. The device sends the recorded audio message in response to detecting a movement of the device. The device removes messages from a conversation based on expiration criteria. The device shares a location with one or more message participants in a conversation.

Abstract: A device provides user interfaces for capturing and sending media, such as audio, video, or images, from within a message application. The device detects a movement of the device and in response, plays or records an audio message. The device detects a movement of the device and in response, sends a recorded audio message. The device removes messages from a conversation based on expiration criteria. The device shares a location with one or more message participants in a conversation.

Abstract: Implementations are disclosed for validating data retrieved from a calibration database. In some implementations, calibrated magnetometer data for a magnetometer of a mobile device is retrieved from a calibration database and validated by data from another positioning system, such as course or heading data provided by a satellite-based positioning system. In some implementations, one or more context keys are used to retrieve magnetometer calibration data from a calibration database that is valid for a particular context of the mobile device, such as when the mobile device is mounted in a vehicle. In some implementations, currently retrieved calibration data is compared with previously retrieved calibration data to determine if the currently retrieved calibration data is valid.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Abstract: Ad hoc data backup for mobile devices is disclosed. When the user of a mobile device has poor or no data connectivity with a network-based storage system and friends are identified that are in the vicinity of the user, backup data is transferred from the user's mobile device to one or more of the friend devices using peer-to-peer connections.

Abstract: Techniques for mobile devices to subscribe and share raw sensor data are provided. The raw sensor data associated with sensors (e.g., accelerometers, gyroscopes, compasses, pedometers, pressure sensors, audio sensors, light sensors, barometers) of a mobile device can be used to determine the movement or activity of a user. By sharing the raw or compressed sensor data with other computing devices, the other computing devices can determine a motion state based on the sensor data. Additionally, in some instances, the other computing devices can determine a functional state based on the sensor data and the motion state. For example, functional state classification can be associated with each motion state (e.g., driving, walking) by further describing each motion state (e.g., walking on rough terrain, driving while texting).

Abstract: Techniques for controlling motions using motion recognizers generated in advance by users are described. According to embodiment, the motion recognizers created by end users are utilized to control virtual objects displayed in a virtual environment. By manipulating one or more motion sensitive devices, end users could command what the objects to do in the virtual environment. Motion signals from each of the motion sensitive devices are recognized in accordance with the motion recognizers created in advance by the users. One or more of the motion signals are at the same time utilized to tune the motion recognizers or create additional motion recognizers. As a result, the motion recognizers are constantly updated to be more accommodating to the user(s).

Abstract: A humanized navigation system provides humanized instructions that mimic a real human navigator, focuses on comprehension rather than precision, and attempts to make the navigation session less stressful for the user. In some implementations, complex navigation situations are classified according to shared common navigation problems. Once a class is determined, humanized navigation instructions are generated and/or selected based on the class and the current location of the user. The humanized navigation instructions include information to aid the user in navigating a route.

Abstract: In some implementations, a mobile device can be configured to provide simplified audio navigation instructions. The simplified audio navigation instructions can provide a reduced set of audio navigation instructions so that the audio instructions are only presented to the user when the user wishes to or needs to hear the instructions. A user can enable the simplified audio navigation instructions. The simplified audio navigation instructions can be enabled automatically. The simplified audio navigation instructions can be configured with rules for when to present audio navigation instructions. For example, the rules can specify that audio navigation instructions are to be provided for complex road segments, a user defined portion of a route, or specified road types, among other criteria. The mobile device can be configured with exceptions to the rules such that audio navigation instructions can be presented when the user has, for example, deviated from a defined route.

Abstract: Methods and mobile devices determine an exit from a vehicle. Sensors of a mobile device can be used to determine when the user is in a vehicle that is driving. The same or different sensors can be used to identify a disturbance (e.g., loss of communication connection from mobile device to a car computer). After the disturbance, an exit confidence score can be determined at various times, and compared to a threshold. A determination of the exit of the user can be determined based on the comparison of the exit confidence score to the threshold. The mobile device can perform one or more functions in response to the exit confidence score exceeding the threshold, such as changing a user interface (e.g., of a navigation app) or obtaining a location to designate a parking location.

Abstract: Methods, program products, and systems of motion-based device operations are described. A mobile device can coordinate operations of a motion sensor and a proximity sensor. The mobile device can determine a gesture event using the motion sensor. The mobile device can determine a proximity event using the proximity sensor. The mobile device can use the gesture event and proximity event to confirm one another, and determine that the mobile device has moved in proximity to a target object following a specified gesture. Upon confirmation, the mobile device can perform a specified task.

Abstract: Motion sensors of a mobile device mounted to a vehicle are used to detect a mount angle of the mobile device. The motion sensors are used to determine whether the vehicle is accelerating or de-accelerating, whether the vehicle is turning and whether the mount angle of the mobile device is rotating. The mount angle of the mobile device is obtained from data output from the motion sensors and can be used to correct a compass heading. Data from the motion sensors that are obtained while the vehicle is turning or the mobile device is rotating are not used to obtain the mount angle.

Abstract: Implementations are disclosed for validating data retrieved from a calibration database. In some implementations, calibrated magnetometer data for a magnetometer of a mobile device is retrieved from a calibration database and validated by data from another positioning system, such as course or heading data provided by a satellite-based positioning system. In some implementations, one or more context keys are used to retrieve magnetometer calibration data from a calibration database that is valid for a particular context of the mobile device, such as when the mobile device is mounted in a vehicle. In some implementations, currently retrieved calibration data is compared with previously retrieved calibration data to determine if the currently retrieved calibration data is valid.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a presumed activity associated with a mobile device. A plurality of sensor values detected by one or more sensors onboard the mobile device is received over a period of time. A plurality of derived values is calculated from the plurality of sensor values. The derived values are selectively combined to generate one or more abstract values. A presumed activity is identified from a plurality of possible activities based on a level of similarity between the one or more abstract values and expected values of each of the plurality of possible activities that correspond to the one or more abstract values.