Stikic Maja, Diane Larlus, Ebert Sandra, Schiele Bernt
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) - scholarly archival journal published monthly
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 datasets 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.
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