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Activity Recognition Using a Few Label Samples

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)

Abstract

Sensor-based human activity recognition aims to automatically identify human activities from a series of sensor observations, which is a crucial task for supporting wide range applications. Typically, given sufficient training examples for all activities (or activity classes), supervised learning techniques have been applied to build a classification model using sufficient training samples for differentiating various activities. However, it is often impractical to manually label large amounts of training data for each individual activities. As such, semi-supervised learning techniques sound promising alternatives as they have been designed to utilize a small training set L, enhanced by a large unlabeled set U. However, we observe that directly applying semi-supervised learning techniques may not produce accurate classification. In this paper, we have designed a novel dynamic temporal extension technique to extend L into a bigger training set, and then build a final semi-supervised learning model for more accurate classification. Extensive experiments demonstrate that our proposed technique outperforms existing 7 state-of-the-art supervised learning and semi-supervised learning techniques.

Keywords

Activity Recognition Semi-Supervised Learning Dynamic Temporal Extension 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Data Analytics DepartmentInstitute for Infocomm Research, A*StarSingapore

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