Abstract:
In an example method, a mobile device connects a voice call for a user. The voice call causes one or more radio frequency transmitters of the mobile device to transmit radio waves at a first power level. Motion data describing movement of the mobile device is obtained, and the orientation of the mobile device is determined based on the motion data. A determination whether the mobile device is on the user's body or on an inanimate object is made based on the orientation of the mobile device over the period of time. The transmit power level is adjusted based on the determination.
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:
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:
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:
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:
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 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.