Systems, methods, and apparatuses for classifying user activity using combining of likelihood function values in a mobile device

A technology of likelihood function and mobile device, which is applied in branch office equipment, transmission system, measuring device, etc.

Inactive Publication Date: 2013-12-18
QUALCOMM INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when estimating a user's activity category, there may be a trade-off between performing an accurate estimate of the user's activity and performing the estimation in a timely fashion

Method used

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  • Systems, methods, and apparatuses for classifying user activity using combining of likelihood function values in a mobile device
  • Systems, methods, and apparatuses for classifying user activity using combining of likelihood function values in a mobile device
  • Systems, methods, and apparatuses for classifying user activity using combining of likelihood function values in a mobile device

Examples

Experimental program
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Embodiment Construction

[0016] In particular embodiments, the classifier may infer a mobile device user's activity category based at least in part on signals received from inertial sensors on the mobile device. In certain examples, signals from one or more inertial sensors may be processed to calculate or extract "signatures" that may indicate or suggest a particular class of activity. The features computed from the inertial sensors can then be applied to an activity estimator to estimate the current activity. The output state of the activity estimator at a given time can be combined with previous output states and filtered to increase the confidence factor of the inference of the activity classification.

[0017] Classifier latency may be defined as the total duration of observed consecutive sensor output states before the classifier produces an inference of the user's activity category. For example, higher latency may result in a higher confidence factor in activity classification due to filtering...

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PUM

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Abstract

Components, methods, and apparatuses are provided that may be used to determining activity likelihood function values for the activity classification for two or more past epochs based, at least in part, on signals from one or more sensors of a mobile device. For example, a method may comprise, for each of a plurality of activity classifications, determining activity likelihood function values for each of the plurality of activity classifications for two or more past epochs. The activity likelihood function values are based, at least in part, on signals from one or more sensors of a mobile device. The method may also include combining the activity likelihood function values to determine a likelihood function for an activity classification at a present epoch. The method may also include inferring a present activity of a user co-located with the mobile device to be one of the activity classifications based, at least in part, on the determined likelihood functions for the activity classifications at the present epoch.

Description

technical field [0001] The subject matter disclosed herein relates to classifying user activity in mobile devices. Background technique [0002] Many mobile communication devices, such as smartphones, contain inertial sensors, such as accelerometers, that can be used to detect motion of the device. These movements can be used to determine the orientation of the device so that the display can be properly oriented, for example in portrait or landscape mode, when displaying information to the user. In another example, a gaming application executed by means of a smartphone may rely on movement detected by one or more accelerometers so that features of the game may be controlled. In other examples, gestural movement detected by the accelerometer may allow the user to scroll a map, navigate menus, or control other aspects of the operation of the device. [0003] While it can be used to assist simple user interface tasks, it has not been possible to provide more complex and meani...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00A61B5/11
CPCA61B5/1123G06F2218/12G06F18/25A61B5/103G01P13/00G01P15/00G16C10/00H04M1/725H04L65/40G06N7/01G06N7/00G06V40/25
Inventor 里昂纳德·亨利·葛罗科普安东尼·沙尔桑吉夫·南达
Owner QUALCOMM INC
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