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.

References

[1]

Wilhelm Kleiminger, Christian Beckel, Thorsten Staake, and Silvia Santini. Occupancy detection from electricity consumption data. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 10:1--10:8, New York, NY, USA, 2013. ACM.

Digital Library

[2]

Andrés Molina-Markham, Prashant Shenoy, Kevin Fu, Emmanuel Cecchet, and David Irwin. Private memoirs of a smart meter. In Proceedings of the 2Nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys '10, pages 61--66, New York, NY, USA, 2010. ACM.

Digital Library

[3]

Erik Buchmann, Klemens Böhm, Thorben Burghardt, and Stephan Kessler. Re-identification of smart meter data. Personal and Ubiquitous Computing, 17(4):653--662, 2013.

Digital Library

[4]

Michael Zeifman and Kurt Roth. Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consumer Electronics, 57(1):76--84, 2011.

Digital Library

[5]

F. Sultanem. Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery, 6(4):1380--1385, Oct 1991.

[6]

J. Zico Kolter and Tommi S. Jaakkola. Approximate inference in additive factorial hmms with application to energy disaggregation. In Neil D. Lawrence and Mark A. Girolami, editors, AISTATS, volume 22 of JMLR Proceedings, pages 1472--1482. JMLR.org, 2012.

[7]

Hyungsul Kim, Manish Marwah, Martin F. Arlitt, Geoff Lyon, and Jiawei Han. Unsupervised disaggregation of low frequency power measurements. In SDM, pages 747--758. SIAM / Omnipress, 2011.

[8]

Christian Beckel, Wilhelm Kleiminger, Romano Cicchetti, Thorsten Staake, and Silvia Santini. The eco data set and the performance of non-intrusive load monitoring algorithms. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys '14, pages 80--89, New York, NY, USA, 2014. ACM.

Digital Library

[9]

Feng Chen, Jing Dai, Bingsheng Wang, Sambit Sahu, Milind Naphade, and Chang-Tien Lu. Activity analysis based on low sample rate smart meters. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, pages 240--248, New York, NY, USA, 2011. ACM.

Digital Library

[10]

K. Carrie Armel, Abhay Gupta, Gireesh Shrimali, and Adrian Albert. Is disaggregation the holy grail of energy efficiency? the case of electricity. Energy Policy, 52(C):213--234, 2013.

[11]

J. Z. Kolter, Siddharth Batra, and Andrew Y. Ng. Energy disaggregation via discriminative sparse coding. In J. Lafferty, C. Williams, J. Shawe-taylor, R.s. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23, pages 1153--1161. 2010.

Digital Library

[12]

Nuria Oliver, Ashutosh Garg, and Eric Horvitz. Layered representations for learning and inferring office activity from multiple sensory channels. Comput. Vis. Image Underst., 96(2):163--180, November 2004.

Digital Library

[13]

Thomas S. Ferguson. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2):209--230, 1973.

[14]

Bela A. Frigyik, Amol Kapila, and Maya R. Gupta. Introduction to the dirichlet distribution and related processes. Technical report, 2010.

[15]

D. Blackwell and J. MacQueen. Ferguson distributions via Polya urn schemes. The Annals of Statistics, 1:353--355, 1973.

[16]

D.J. Aldous. Exchangeability and related topics. In École d' Été St Flour 1983, pages 1--198. Springer-Verlag, 1985. Lecture Notes in Math. 1117.

[17]

Jayaram Sethuraman. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639--650, 1994.

[18]

Carl Edward Rasmussen. The infinite gaussian mixture model. In In Advances in Neural Information Processing Systems 12, pages 554--560. MIT Press, 2000.

Digital Library

[19]

Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, and David M. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101, 2004.

[20]

Y. W. Teh, D. Gorur, and Z. Ghahramani. Stick-breaking construction for the Indian buffet process. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 11, pages 556--63, Brookline: Massachusetts, 2007. Microtone.

[21]

E. B. Fox, E. B. Sudderth, M. I. Jordan, and A. S. Willsky. An HDP-HMM for systems with state persistence. In Proc. International Conference on Machine Learning, July 2008.

Digital Library

[22]

E.B. Fox, E.B. Sudderth, M.I. Jordan, and A.S. Willsky. A sticky HDP-HMM with application to speaker diarization. Annals of Applied Statistics, 5(2A):1020--1056, 2011.

[23]

Matthew J. Johnson and Alan S. Willsky. The hierarchical dirichlet process hidden semi-markov model. CoRR, abs/1203.3485, 2012.

Digital Library

[24]

Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon, and Tai Sing Lee. The block diagonal infinite hidden markov model. In David A. Van Dyk and Max Welling, editors, AISTATS, volume 5 of JMLR Proceedings, pages 552--559. JMLR.org, 2009.

[25]

Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. Frequent pattern mining: Current status and future directions. Data Min. Knowl. Discov., 15(1):55--86, August 2007.

Digital Library

[26]

Martin Ester, Hans peter Kriegel, Jörg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. pages 226--231. AAAI Press, 1996.

Digital Library

[27]

Erik B. Sudderth. Graphical Models for Visual Object Recognition and Tracking. PhD thesis, Cambridge, MA, USA, 2006. AAI0809973.

Digital Library

[28]

Xiao JingHui, Liu BingQuan, and Wang XiaoLong. Principles of non-stationary hidden markov model and its applications to sequence labelling task. Natural Language Processing - IJCNLP, 3651:827--837, 2005.

Digital Library

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
Show More Cited By

Detecting Activities of Daily Living from Low Frequency Power Consumption Data
1. Computing methodologies

Recommendations

HMM machine learning and inference for Activities of Daily Living recognition

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person's functional status. Many recent researches concentrate ...
AnomalyByClick: An Interactive Visualization Tool for Monitoring Activities of Daily Living and Anomaly Annotation
AVI '22: Proceedings of the 2022 International Conference on Advanced Visual Interfaces

We present AnomalyByClick, an interactive visualization system that allows the monitoring and analysis of anomalies in the behavior of elderly patients during daily activities in a living environment.
Modelling and simulation of activities of daily living representing an older adult's behaviour
PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

The availability of datasets for monitoring the activities of daily living is limited by difficulties associated with the collection of such data. There have been many suggested software solutions to overcome this issue. In this paper, a new technique ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

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]

In-Cooperation

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MOBIQUITOUS 2016

MOBIQUITOUS 2016: Computing, Networking and Services

November 28 - December 1, 2016

Hiroshima, Japan

Acceptance Rates

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

Overall Acceptance Rate 26 of 87 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
120
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents