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Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household

Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household

Long Niu, Sachio Saiki, Masahide Nakamura
Copyright: © 2018 |Volume: 6 |Issue: 4 |Pages: 14
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522546863|DOI: 10.4018/IJSI.2018100102
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MLA

Niu, Long, et al. "Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household." IJSI vol.6, no.4 2018: pp.16-29. http://doi.org/10.4018/IJSI.2018100102

APA

Niu, L., Saiki, S., & Nakamura, M. (2018). Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household. International Journal of Software Innovation (IJSI), 6(4), 16-29. http://doi.org/10.4018/IJSI.2018100102

Chicago

Niu, Long, Sachio Saiki, and Masahide Nakamura. "Using Non-Intrusive Environmental Sensing for ADLS Recognition in One-Person Household," International Journal of Software Innovation (IJSI) 6, no.4: 16-29. http://doi.org/10.4018/IJSI.2018100102

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Abstract

This article describes how pervasive sensing technologies are promising for increasing one-person household (OPH), where a system monitors and assists a resident to maintain healthy life rhythm. Automatic recognition of activities of daily living (ADLS) has been a hot research topic in pervasive computing. However, most existing methods have limitations in development cost, privacy exposure, and inconvenience for residents. To cope with the limitations, this article presents a new ADL recognition system especially for OPH. To minimize the development cost as well as intrusions to user and house, the system exploits an IoT-based environment sensing device, called autonomous sensor box (sensorbox) which can autonomously measure 7 kinds of environment attributes. The system then applies machine-learning techniques to predict 7 kinds of ADLS. Finally, this article conducts an experiment within an actual apartment of a single user. The result shows that the proposed system achieves the average accuracy of ADLS recognition with about 88%, by carefully developing the features of environment attributes.

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