PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
Maja Stikic, Diane Larlus, Sandra Ebert and Bernt Schiele
IEEE Trans. Pattern Anal. Mach. Intell. Volume 33, Number 12, pp. 2521-2537, 2011.

Abstract

This paper considers scalable and unobtrusive activity recognition using on-body sensing for context awareness in wearable computing. Common methods for activity recognition rely on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is challenging, preventing the applicability of these approaches in real-world settings. This paper proposes new annotation strategies that substantially reduce the required amount of annotation. We explore two learning schemes for activity recognition that effectively leverage such sparsely labeled data together with more easily obtainable unlabeled data. Experimental results on two public data sets indicate that both approaches obtain results close to fully supervised techniques. The proposed methods are robust to the presence of erroneous labels occurring in real-world annotation data.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:8957
Deposited By:Diane Larlus
Deposited On:21 February 2012