Towards Less Supervision in Activity Recognition from Wearable Sensors

10 years 7 months ago
Towards Less Supervision in Activity Recognition from Wearable Sensors
Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity recognition rely on supervised learning techniques limiting their applicability in real-world scenarios and their scalability to large amounts of activities and training data. State-of-the-art activity recognition algorithms can roughly be divided in two groups concerning the choice of the classifier, one group using generative models and the other discriminative approaches. This paper presents a method for activity recognition which combines a generative model with a discriminative classifier in an integrated approach. The generative part of the algorithm allows to extract and learn structure in activity data without any labeling or supervision. The discriminant part then uses a small but labeled subset of the training data to train a discriminant classifier. In experiments we show that this scheme enables...
Tâm Huynh, Bernt Schiele
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where ISWC
Authors Tâm Huynh, Bernt Schiele
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