Implementation of a Flexible Bayesian Classifier for the Assessment of Patient’s Activities within a Real-time Personalized Mobile Application
Abstract
This paper presents an implementation of a mobile application that provides a real-time personalized assessment of patient’s activities by using a Flexible Bayesian Classifier. The personalized assessment is derived from data collected from the 3-axial accelerometer sensor and the counting steps sensor, both widespread among nowadays mobile devices. Despite the fact that online mobile solutions with Bayesian Classifier have been rare and insufficiently precise, we have proven that the accuracy of the proposed system within a defined data model is comparable to the accuracy of decision trees and neural networks.
Keywords:
activity recognition, real-time mobile application, Flexible Bayesian ClassifierDownloads
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