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Topic based feature construction for activity recognition. (Chinese. English summary) Zbl 1363.68144
Summary: A topic based feature construction method was proposed to address the problem that traditional activity recognition methods based on feature extraction heavily depend on the domain knowledge of researchers and the quantity of the training dataset. Based on symbolic aggregate approximation (SAX), the proposed method employed a topic model to discover activity patterns. After being preprocessed by dimensionality reduction techniques and SAX, the acceleration data were used as document set for the topic model. Pattern mining was completed through topical model to reduce the dimensions of the document data and construct the latent topic related vectors, then vector space model (VSM) was utilized to classify different activities. Results show that SAX based topic model can be well applied on activity recognition, and the proposed method is more effective to improve the recognition accuracy than feature extraction based method and motif discovery based method.
MSC:
68T10 Pattern recognition, speech recognition
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