Non-parametric modeling apparatus and method for classification, especially of activity state
AssigneeVenture Gain LLC
InventorJack E. Mott
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Abstract
The activity state classification method of the present invention employs a kernel-based modeling technique, and more specifically a set of similarity-based models, which have been created using example data, to process an input observation or set of input observations, each comprising a set of sensor readings or “features” derived there from or other data, to predict the activity state of a person from whom the sensor data was obtained. A model is created for each class of activity. The input data is processed by each model and the resulting predictions are combined to yield a final prediction of which state of activity is represented by the input data.
Claims19
A method for determining the class of an activity of a person, selected from a plurality of activities of interest, based on multivariate sensor data comprising values or features derived from values of sensors measuring physiological parameters from the person, comprising the steps of:
- providing a plurality of kernel-based models, each corresponding to one of said activities of interest, and each model comprising a plurality of reference observations of said multivariate sensor data, at least some said reference observations having been acquired from sensors on a person during a modeled activity to which said model corresponds, and at least some said reference observations having been acquired from sensors on a person during activity different from that to which said model corresponds, and all said reference observations further having a class membership value corresponding to whether said reference observation is of said modeled activity of not;
- obtaining a new observation of readings of said multivariate sensor data;
- generating in a computer processor an inferential estimate of said class membership value for each of at least some of said kernel-based models using said new observation as input to the at least some of said kernel-based models, where said inferential estimate for a given kernel-based model is generated from a linear combination of at least some of said reference observations of said given kernel-based model;
- and
- determining the class of activity corresponding to said new observation based on a comparison of the class membership estimates.
Dependencies are inferred from the claim text.