Abstract:
Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for scheduling an execution of a task, such as a non-real time, non-latency sensitive background task on a computing device. In one implementation, the technique includes detecting a first state of a device, wherein the first state of the device is associated with a first power level and a first task, wherein the first power level is at least partially based on power consumption of a first task, determining that the first power level associated with the first state is above a threshold, and in response to determining that the first power level associated with the first state is above the threshold, and scheduling an execution of a second task on the device, wherein the second task is associated with automatically collecting of calibration data using at least one sensor.
Abstract:
Methods, systems, computer-readable media, and apparatuses for tap detection in a mobile device are presented. In some embodiments, the method may comprise storing, by a mobile device, a first data sample from an accelerometer sensor and a second data sample from a gyroscope sensor. Additionally, the method may comprise processing a plurality of data samples. The plurality of data samples can include the first data sample or the second data sample. Optionally, in one embodiment, the method may comprise suppressing a tap that has been classified as a false detection based on at least one of the plurality of data samples. Subsequently, the method may comprise determining an occurrence of a tap at a mobile device based on the results of the processing.
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:
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:
Step detection accuracy in mobile devices is increased by determining whether swinging is taking place. According to the invention, swinging can be detected using threshold detection, Eigen analysis, hybrid frequency analysis, and/or gyroscope-based analysis, for example. The determination that swinging is (or may be) occurring can impact how the mobile device reports detected steps for step detection. A count of missteps and/or a level of certainty, based on swing detection, can be provided with a step count.
Abstract:
Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.
Abstract:
Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for improving calibration data by increasing the diversity of orientations used for generating the calibration data. In one embodiment, the computing device receives a plurality of calibration measurements associated with one or more sensors of a device, determines a degree to which the plurality of calibration measurements were captured at different orientations of the device, and determines, based on the degree, whether to update one or more calibration parameters.
Abstract:
Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media to improve the calibration data by taking into account the effects of change in temperature on motion sensors. For instance, different levels of error may be associated with a motion sensor at different temperature levels. In one implementation, the sensor measurement data associated with the various orientations at a temperature is used in determining the calibration data for that temperature.