NDLI: New approaches for discovering unsupervised human activities by mining sensor data

Content Provider	IEEE Xplore Digital Library
Author	Chitturi, Bhadrachalam Thomas, Jyothi Indulekha T. S.
Copyright Year	2015
Description	Author affiliation: Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, India (Chitturi, Bhadrachalam; Thomas, Jyothi; Indulekha T. S.)
Abstract	Discovering human activities facilitates computerization and the consequent monitoring of the smart home environment. The existing unsupervised human activity discovery systems perform segmentation clustering followed by the labeling of the sensor data. Segmentation clustering consists of forming segments from similar consecutive frames and then clustering similar segments. A cluster is labeled by the action associated with its most frequent sensor(s). In these methods, even if similar segments denote distinct activities they often occur in the same cluster. We propose three alternate methods to address this issue. The first method is a minor variant of the segmentation clustering where subsequences of the segments are clustered instead of the segments. We employ the concept of cover where (a, b, c) subsumes (a, c) if they have identical frequency. The second method employs a new algorithm, i.e. LRS, instead of segmentation. The third method is a hybrid method that extracts subsequences from the output of LRS. We compared the proposed systems with the existing system on CASAS dataset, a real world human activity dataset. The third method that employs LRS followed by subsequence extraction yielded the best Dunn's index and the best correctness in clusters as per confusion matrix.
Starting Page	118
Ending Page	123
File Size	529852
Page Count	6
File Format	PDF
e-ISBN	9781467373098
DOI	10.1109/CoCoNet.2015.7411176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Algorithms Clustering algorithms Smart homes Elder care Assisted living Labeling Indexes Human activity detection Time complexity Read only memory Unsupervised learning
Content Type	Text
Resource Type	Article

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