Abstract:
Disclosed is a system, apparatus, computer readable storage medium, and method to perform a transition triggered context monitoring for a mobile device. A first sensor data stream comprising data from one or more sensors at the mobile device is received. One or more features calculated from the data of the first sensor data stream may be monitored and a status change for the one or more features is detected. In response to detecting the status change, of a second sensor data stream comprising data from one or more sensors at the mobile device is collected. The second sensor data stream may be processed as a context label for a segment of the first sensor data stream and the segment beginning may be defined by the status change.
Abstract:
System and methods are disclosed to use information available on the state of mobile devices in a heuristics-based approach to improve motion state detection. In one or more embodiments, information on the WiFi connectivity of mobile de vices may be used to improve the detection of the in-transit state. The WiFi connectivity information may be used with sensor signal such as accelerometer signals in a motion classifier to reduce the false positives of the in-transit state. In one or more embodiments, information that a mobile device is connected to a WiFi access point (AP) may be used as heuristics to reduce the probability of falsely classifying the mobile device in the in-transit state when mobile device is actually in the hand of a relatively stationary user. Information on the battery charging state or (he wireless connectivity of the mobile devices may also be used to improve the detection of in-transit state.
Abstract:
Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.
Abstract:
A method of adjusting a controller setting of a mobile device is presented. The method includes determining a user context based on information generated from one or more data sources. The method also includes adjusting the controller setting based on the user context.
Abstract:
Disclosed is an apparatus and method for classifying a motion state of a mobile device. In one embodiment, accelerometer data representing acceleration components along orthogonal x, y, and z axes of the mobile device are collected. A presence or absence of a half-step frequency relationship between the accelerometer data is determined. Last, the motion state of the device is determined based at least in part on the presence or absence of the half-step frequency relationship.
Abstract:
Embodiments of the present invention are directed toward providing intelligent sampling strategies that make efficient use of an always-on camera. To do so, embodiments can utilize sensor information to determine contextual information regarding the mobile device and/or a user of the mobile device. A sampling rate of the always-on camera can then be modulated based on the contextual information.
Abstract:
Methods, systems, computer-readable media, and apparatuses for determining indoor/outdoor state of a mobile device are presented. In some embodiments, a mobile device may maintain an indoor/outdoor state. The mobile device may include at least one first sensor and at least one second sensor, the first sensor associated with higher power consumption than the second sensor. The mobile device may gate off the first sensor and using the second sensor to obtain a sensor reading, if the second sensor can generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may use the first sensor to obtain a sensor reading, if the second sensor cannot generate a reading indicative of the indoor/outdoor state of the mobile device. The mobile device may update the indoor/outdoor state of the mobile device based on a reading received from one of the first and the second sensors.
Abstract:
Sensor data from a sensor system of a mobile device may be used for determining a level of pressure exerted by a user on the mobile device. The sensor system may include one or more types of sensors, such as a microphone and one or more inertial sensors. The inertial sensors may include one or more gyroscopes and/or accelerometers. Based on the inertial sensor data, it may be determined whether and/or how the mobile device is being held. A process for determining a level of pressure exerted by a user on the mobile device may be adapted based, at least in part, on whether and/or how the mobile device is being held. The pressure determining process may be adapted according to various other criteria, such as a position of a touch target in a display, ambient noise levels, etc.
Abstract:
Apparatuses and methods for detecting imminent use of a device are disclosed. According to aspects of the present disclosure, a device can be configured to consume sensor data, such as accelerometer data, or other available information obtained from low power sources. From the sensor data or other available information, the device is configured to determine an inference of imminent use. Based on the determination of inference of imminent use, the device can be configured to provide information for power management applications or situation aware applications in some implementations.
Abstract:
Disclosed is an apparatus and method for motion classification using a combination of low-power sensor data and modem information. In one embodiment, data received from at least one low-power sensor is collected. Information regarding cellular network signals is collected from a modem. A speed estimate is determined based on the information regarding cellular network signals. A motion context classification is then determined based on a combination of the collected data received from the at least one low-power sensor and the speed estimate.