Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection

Abstract

In recent years, on-body device position recognition has attracted a lot of attention from the ubiquitous computing community with a view to providing reliable services to users. The existing work has focused on the recognition of classes included in a training dataset, but handling a new position that the recognition system does not know is still impossible. The unknown position should be handled in an appropriate way to avoid incorrect behavior and adapt to each user's way of carrying the device. In this article, we propose a new detection method based on the ensemble learning principle, in which the final results are obtained from a collection of judgments by a weak novelty detector. We devise a method of finding a threshold that maximizes overall accuracy, rather than a mere majority vote. This method is evaluated with three datasets and various conditions to confirm the effectiveness of ensemble novelty detection and the threshold estimation method.

New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System

“…In the design of a novelty detection component (A), we compared three popular novelty detection methods, i.e., one-class Support Vector Machine (OCSVM) [ 40 ], local outlier factor (LOF) [ 41 ], and isolation forest (IForest) [ 42 ], and we concluded that LOF is applicable here [ 7 ]. Furthermore, an ensemble novelty detection method is proposed, in which we showed that the accuracy of discrimination between the unknown and known positions was improved [ 43 ].…”