期刊
NEUROCOMPUTING
卷 181, 期 -, 页码 108-115出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.08.096
关键词
Activity recognition; Temporal pattern mining; Sensor-generated data; Discriminative feature extraction
资金
- National University of Singapore [R-252-000-473-133, R-252-000-473-750]
As compared to actions, activities are much more complex, but semantically they are more representative of a human's real life. Techniques for action recognition from sensor-generated data are mature. However, few efforts have targeted sensor-based activity recognition. In this paper, we present an efficient algorithm to identify temporal patterns among actions and utilize the identified patterns to represent activities for automated recognition. Experiments on a real-world dataset demonstrated that our approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation. (C) 2015 Elsevier B.V. All rights reserved.
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