Abstract:
A method includes receiving cellular network signals at a mobile device from several cells of a cellular network. The method then includes generating a place model representative of a characteristic of the place where the mobile device is located in response to the received cellular network signals. In one aspect, the place model is clustered with one or more previously generated place models if the place model is similar to the one or more previously generated place models. In another aspect, it is determined whether the place where the mobile device is located is a place of relevance to a user based on the clustering of one or more previously generated place models if the place model is similar to the one or more previously generated place models.
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.
Abstract:
A method of operating a shared resource in a mobile device includes extracting a set of features from a plurality of subsystems of the mobile device. The set of features may be extracted from each subsystem of the plurality of subsystems requesting services from one or more shared resources of the mobile device. One or more parameter of the shared resource(s) may be determined based on the extracted set of features from the plurality of subsystems. The shared resource(s) may be operated based on the determined parameter(s).
Abstract:
Systems, apparatus and methods in a mobile device to enable and disable a depth sensor for tracking pose of the mobile device are presented. A mobile device relaying on a camera without a depth sensor may provide inadequate pose estimates, for example, in low light situations. A mobile device with a depth sensor uses substantial power when the depth sensor is enabled. Embodiments described herein enable a depth sensor only when images are expected to be inadequate, for example, accelerating or moving too fast, when inertial sensor measurements are too noisy, light levels are too low or high, an image is too blurry, or a rate of images is too slow. By only using a depth sensor when images are expected to be inadequate, battery power in the mobile device may be conserved and pose estimations may still be maintained.
Abstract:
A context aware system, for use in a mobile device, includes a context change detector (CCD) coupled to a context classifier (CCL). The CCD is configured to receive sensor data and to detect a change in a current context state of the mobile device based on the received sensor data. The CCL is configured to transition from a low power consumption mode to a normal power consumption mode in response to the CCD detecting the change in the current context state. The CCL is further configured to determine a next context state of the mobile device while in the normal power consumption mode.
Abstract:
Disclosed is an apparatus and method for classifying a motion state of a mobile device comprising: determining a first motion state associated with a highest probability value and with a first confidence level greater than a first threshold; entering the first motion state; while the first motion state is active, determining a second motion state associated with a highest probability value and with a second confidence level greater than the first threshold, the second motion state being different from the first motion state; determining whether the second motion state is to be entered; and in response to determining that the second motion state is to be entered, entering the second motion 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:
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 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.