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 devices 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 the wireless connectivity of the mobile devices may also be used to improve the detection of in-transit state.
Abstract:
The disclosure is directed to modifying the operation of one or more hardware subsystems when a new context awareness service begins. An aspect determines a power budget for a plurality of operating context awareness services including the new context awareness service, wherein the power budget is based on a power requirement for each of the plurality of context awareness services, and wherein the power requirement for each of the plurality of context awareness services is based on power utilizations of the one or more hardware subsystems corresponding to the plurality of context awareness services, and allocates power resources to the one or more hardware subsystems based on importances of the plurality of context awareness services and/or the one or more hardware subsystems, wherein the allocation of the power resources is performed within the power budget.
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:
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:
Systems and methods share context information on a neighbor aware network. In one aspect, a context providing device receives a plurality of responses to a discovery query from a context consuming device, and tailors services it offers to the context consuming device based on the responses. In another aspect, a context providing device indicates in its response to a discovery query which services or local context information it can provide to the context consuming device, and also a cost associated with providing the service or the local context information. In some aspects, the cost is in units of monetary currency. In other aspects, the cost is in units of user interface display made available to an entity associated with the context providing device in exchange for the services or local context information offered to the context consuming device.
Abstract:
Disclosed is a system, apparatus, computer readable storage medium, and method to perform a context inference for a mobile device. In one embodiment, a data processing system includes a processor and a storage device configurable to store instructions to perform a context inference for the data processing system. Data may be received from at least a first sensor, and a first classification of the data from the sensor may be performed. Confidence for the first classification can be determined and a second sensor can be activated based on a determination that the confidence fails to meet a confidence threshold. A data sample classification from the activated second sensor may be classified jointly with the data from first sensor.