research-article

Detecting Activities of Daily Living from Low Frequency Power Consumption Data

Authors:

Chihiro OnoAuthors Info & Claims

MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 38 - 46

https://doi.org/10.1145/2994374.2994376

Published: 28 November 2016 Publication History

Abstract

With the popularization of smart sensors, detecting activities of daily living (ADL) from sensor readings has attracted many interests in both the academic and the industrial societies. A majority of research works on this topic focus on data of high sampling rate, however, most existing smart sensor deployments support sampling rate much lower than 1 Hz. We are interested in the possibility of inferring ADLs from solely coarse home-level gross power consumptions. In this paper, we first tentatively adopt a layered hidden Markov model (LHMM) in the hope to uncover the association between ADLs and power consumption data. We conduct an exploratory data analysis with this preliminary model on a real-world dataset, and based on the findings from this exploratory study, we propose to infer ADLs from low frequency power consumption data using a hierarchical Dirichlet process hidden markov model (HDP-HMM). We perform experiments on the same dataset, and demonstrate that with sensor readings of 1/180 Hz and 1/900 Hz granularities, HDP-HMM outperforms comparative models and ADLs such as "Entertaining" and "Not at home" can be captured with high accuracy.

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Cited By

Jourdan TBoutet ABahi AFrindel C(2020)Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare MonitoringACM Transactions on Computing for Healthcare10.1145/34169472:1(1-22)Online publication date: 30-Dec-2020
https://dl.acm.org/doi/10.1145/3416947
Jourdan TBoutet AFrindel CSchulzrinne HLi P(2018)Toward privacy in IoT mobile devices for activity recognitionProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287009(155-165)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287009
Meurisch CGogel ASchmidt BNolle TJanssen FSchweizer IMühlhäuser MGu TKotagiri RLiu H(2017)Capturing Daily Student Life by Recognizing Complex Activities Using SmartphonesProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144472(156-165)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144472
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Detecting Activities of Daily Living from Low Frequency Power Consumption Data
1. Computing methodologies

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MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2016

307 pages

ISBN:9781450347501

DOI:10.1145/2994374

General Chairs:
Takahiro Hara
Osaka University, Japan
,
Hiroshi Shigeno
Keio University, Japan

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EAI: The European Alliance for Innovation
SIGAPP: ACM Special Interest Group on Applied Computing
SIGBED: ACM Special Interest Group on Embedded Systems
KDDI R&D Laboratories Inc.

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Association for Computing Machinery

New York, NY, United States

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Published: 28 November 2016

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MOBIQUITOUS 2016

MOBIQUITOUS 2016: Computing, Networking and Services

November 28 - December 1, 2016

Hiroshima, Japan

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MOBIQUITOUS 2016 Paper Acceptance Rate 26 of 87 submissions, 30%;

Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

Jourdan TBoutet ABahi AFrindel C(2020)Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare MonitoringACM Transactions on Computing for Healthcare10.1145/34169472:1(1-22)Online publication date: 30-Dec-2020
https://dl.acm.org/doi/10.1145/3416947
Jourdan TBoutet AFrindel CSchulzrinne HLi P(2018)Toward privacy in IoT mobile devices for activity recognitionProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287009(155-165)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287009
Meurisch CGogel ASchmidt BNolle TJanssen FSchweizer IMühlhäuser MGu TKotagiri RLiu H(2017)Capturing Daily Student Life by Recognizing Complex Activities Using SmartphonesProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144472(156-165)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144472
Cheng WErfani SZhang RRamamohanarao KGu TKotagiri RLiu H(2017)Markov Dynamic Subsequence Ensemble for Energy-Efficient Activity RecognitionProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144470(282-291)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144470
Abrishamchi MAbdullah ADavid Cheok ABielawski K(2017)Side channel attacks on smart home systems: A short overviewIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2017.8217429(8144-8149)Online publication date: Oct-2017
https://doi.org/10.1109/IECON.2017.8217429

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