NDLI: A method for recognizing living activities in homes using positioning sensor and power meters

Content Provider	IEEE Xplore Digital Library
Author	Ueda, K. Tamai, M. Yasumoto, K.
Copyright Year	2015
Description	Author affiliation: Nara Inst. of Sci. & Technol., Nara, Japan (Ueda, K.; Tamai, M.; Yasumoto, K.)
Abstract	To realize smart homes with sophisticated services including energy-saving context-aware appliance control in homes and elderly monitoring systems, automatic recognition of human activities in homes is essential. Several daily activity recognition methods have been proposed so far, but most of them still have issues to be solved such as high deployment cost due to many sensors and/or violation of users' feeling of privacy due to use of cameras. Moreover, many activity recognition methods using wearable sensors have been proposed, but they focus on simple human activities like walking, running, etc. and it is difficult to use these methods for recognition of various complex activities in homes. In this paper, we propose a machine learning based method for recognizing various daily activities in homes using only positioning sensors equipped by inhabitants and power meters attached to appliances. To efficiently collect training data for constructing a recognition model, we have developed a tool which visualizes a time series of sensor data and facilitates a user to put labels (activity types) to a specified time interval of the sensor data. We obtain training samples by dividing the extracted training data by a fixed time window and calculating for each sample position and power consumptions averaged over a time window as feature values. Then, the obtained samples are used to construct an activity recognition model by machine learning. Targeting six different activities (watching TV, taking a meal, cooking, reading a book, washing dishes, and other), we applied our proposed method to the sensor data collected in a smart home testbed. As a result, our method recognized 6 different activities with precision of about 85% and recall of about 82%.
Starting Page	354
Ending Page	359
File Size	388990
Page Count	6
File Format	PDF
ISBN	9781479984251
DOI	10.1109/PERCOMW.2015.7134062
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	TV Accuracy Power demand Smart homes Feature extraction Acoustics Training data
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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