A Pattern Mining Approach to Sensor-based Human Activity Recognition

Tao Gu, Liang Wang, Zhanqing Wu, Xianping Tao, Jian Lu

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Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing
due to its potential in many applications such as assistive living and healthcare. This task is particularly challenging because human
activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real
life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent
activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed
concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved and
concurrent activities in a unified framework. We exploit Emerging Pattern—a discriminative pattern that describes significant changes
between classes of data—to identify sensor features for classifying activities. Different from existing learning-based approaches which
require different training datasets for building activity models, our activity models are built upon the sequential activity trace only and can
be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating
the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with
a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 88.1% for interleaved activity and 82.53% for
concurrent activity.
TidsskriftIEEE Transactions on Knowledge and Data Engineering (TKDE)
Udgave nummer9
Sider (fra-til)1359-1372
StatusUdgivet - 2011


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