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Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, the authors propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. They show how to build their activity models based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes.
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