Abstract: Apps may be tagged with location data when they are used. Mobile device may anonymously submit app usage data. Aggregated app usage data from many mobile devices may be analyzed to determine apps that are particularly relevant to a given location (i.e., exhibiting a high degree of localization). Analysis may include determining the app usage intensity, whether hotspots exist or not at a given location, the spatial entropy of a particular app, the device populations in a particular area, etc. Based on the localized app analysis, apps may be ranked according to local relevance, and, based on this ranking, app recommendations may be provided to a user.

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: 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: In some implementations, a computer-implemented method includes receiving a reading from a magnetometer of a mobile device. A cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings is selected. The selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold. An external magnetic field is estimated based on the reading and the representative bias offset for the selected cluster. Whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold is determined.

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: A movable game controller for controlling aspects of a computer controlled game display with apparatus for determining the linear and angular motion of that movable controller. The apparatus includes a plurality of self-contained inertial sensors for sensing the tri-axial linear and tri-axial angular motion of the moving controller. Each sensor is mounted at a fixed linear position and orientation with respect to the others. The linear and angular motion of the controller is computed from the correlated motion sensor readings of each of the plurality of self-contained inertial sensors.

Abstract: In general, in one aspect, a method includes receiving data from one or more motion sensors of a mobile device and calculating, after the period of time, a statistical measurement of the motion sensor data. The method also includes comparing the calculated statistical measurement to one or more threshold values, and, based on the comparing, determining a dynamic state of the mobile device. The method also includes, based on the determined dynamic state, determining an orientation of the mobile device.

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: A video gaming system includes a wireless controller that senses linear and angular acceleration to calculate paths of controller movement over a broad range of controller motion. The system also includes an electromagnetic alignment element, such as a set of LEDS. The controller includes an additional sensor to sense light from the LEDs over a relatively restricted range of controller motion, and use this sensed light to dynamically calibrate the controller when the controller passes through the restricted range of motion over which the sensor senses the light.

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: In general, in one aspect, a method includes receiving data from one or more motion sensors of a mobile device and calculating, after the period of time, a statistical measurement of the motion sensor data. The method also includes comparing the calculated statistical measurement to one or more threshold values, and, based on the comparing, determining a dynamic state of the mobile device. The method also includes, based on the determined dynamic state, determining an orientation of the mobile device.

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 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.