Discovering Activities to Recognize and Track in a Smart Environment

Parisa Rashidi; Diane J Cook; Lawrence B Holder; Maureen Schmitter-Edgecombe

doi:10.1109/TKDE.2010.148

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Discovering Activities to Recognize and Track in a Smart Environment

Journal article

Open access

Discovering Activities to Recognize and Track in a Smart Environment

Parisa Rashidi, Diane J Cook, Lawrence B Holder and Maureen Schmitter-Edgecombe

IEEE transactions on knowledge and data engineering, Vol.23(4), pp.527-539

2011

DOI: https://doi.org/10.1109/TKDE.2010.148

Handle:

https://hdl.handle.net/2376/106756

PMCID: PMC3100559

PMID: 21617742

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Abstract

Activity recognition

Sequence mining

Data mining

Clustering

Smart homes

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been pre-selected and for which labeled training data is available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual’s routine. With this capability we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual’s patterns and lifestyle. In this paper we describe our activity mining and tracking approach and validate our algorithms on data collected in physical smart environments.

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Title: Discovering Activities to Recognize and Track in a Smart Environment
Creators: Parisa Rashidi - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163
Diane J Cook - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163
Lawrence B Holder - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163
Maureen Schmitter-Edgecombe - Department of Psychology, Washington State University, Pullman, WA, 99163
Publication Details: IEEE transactions on knowledge and data engineering, Vol.23(4), pp.527-539
Academic Unit: Psychology, Department of; Electrical Engineering and Computer Science, School of
Identifiers: 99900546755801842
Language: English
Resource Type: Journal article