Detecting anomalies in Activities of Daily Living of elderly residents via energy disaggregation and cox processes

BuildSys 2015 - Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built

Volumn , Issue , 2015, Pages 225-234

  Alcalá, José ^a   Parson, Oliver ^b   Rogers, Alex ^b  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDED SYSTEMS; SMART METERS; VIRTUAL REALITY;

ACTIVITIES OF DAILY LIVING; AGEING POPULATION; DAILY ROUTINES; DISAGGREGATION; FALSE ALARMS; FALSE POSITIVE; LOG-GAUSSIAN COX PROCESS; PRIVACY CONCERNS;

ENERGY EFFICIENCY;

EID: 84959057716     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2821650.2821654     Document Type: Conference Paper

References (26)

1. Larry C Andrews. Special functions for engineers and applied mathematicians. Macmillan, 1985.
2. Yi-Cheng Chen, Yu-Lun Ko, Wen-Chih Peng, and Wang-Chien Lee. Mining appliance usage patterns in smart home environment. In Advances in Knowledge Discovery and Data Mining, volume 7818 of Lecture Notes in Computer Science, pages 99-110. Springer Berlin Heidelberg, 2013.
3. David Roxbee Cox and Valerie Isham. Point processes, volume 12. CRC Press, 1980.
4. Noel Cressie and Christopher K Wikle. Statistics for spatio-temporal data. John Wiley & Sons, 2011.
5. Department of Energy & Climate Change. Smart Metering Equipment Technical Specifications Version 2. Technical report, UK, 2013.
6. Karl Friston, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will Penny. Variational free energy and the laplace approximation. Neuroimage, 34(1):220-234, 2007.
7. G. W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870-1891, 1992.
8. Matthew J Johnson and Alan S Willsky. Bayesian Nonparametric Hidden Semi-Markov Models. Journal of Machine Learning Research, 14:673-701, 2013.
9. H. Kim, M. Marwah, M. Arlitt, G. Lyon, and J. Han. Unsupervised Disaggregation of Low Frequency Power Measurements. In Proceedings of the 11th SIAM International Conference on Data Mining, pages 747-758, Mesa, AZ, USA, 2011.
10. J. Z. Kolter and T. Jaakkola. Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. In Proceedings of the International Conference on Artificial Intelligence and Statistics, pages 1472-1482, La Palma, Canary Islands, 2012.
11. J. Z. Kolter and M. J. Johnson. REDD: A Public Data Set for Energy Disaggregation Research. In ACM Knowledge Discovery and Data Mining, workshop on Data Mining Applications in Sustainability, San Diego, CA, USA, 2011.
12. Joon-Ho Lim, Hyunchul Jang, Jaewon Jang, and Soo-Jun Park. Daily activity recognition system for the elderly using pressure sensors. In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5188-5191, August 2008.
13. Jesper Møller, Anne Randi Syversveen, and Rasmus Plenge Waagepetersen. Log gaussian cox processes. Scandinavian journal of statistics, 25(3):451-482, 1998.
14. Shigeru Ohta, Hiroshi Nakamoto, Yoshimitsu Shinagawa, and Tomohiro Tanikawa. A health monitoring system for elderly people living alone. Journal of telemedicine and telecare, 8(3):151-156, 2002.
15. Oliver Parson, Siddhartha Ghosh, Mark Weal, and Alex Rogers. Non-intrusive load monitoring using prior models of general appliance types. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, pages 356-362, Toronto, ON, Canada, 2012.
16. Oliver Parson, Siddhartha Ghosh, Mark Weal, and Alex Rogers. An Unsupervised Training Method for Non-intrusive Appliance Load Monitoring. Artificial Intelligence, 217:1-19, 2014.
17. P.E. Ross. Managing care through the air [remote health monitoring]. IEEE Spectrum, 41(12):26-31, 2004.
18. Cliodhna Ní Scanaill, Sheila Carew, Pierre Barralon, Norbert Noury, Declan Lyons, and Gerard M. Lyons. A review of approaches to mobility telemonitoring of the elderly in their living environment. Annals of Biomedical Engineering, 34(4):547-563, 2006.
19. Hao Song, G Kalogridis, and Zhong Fan. Short paper: Time-dependent power load disaggregation with applications to daily activity monitoring. In IEEE World Forum on Internet of Things, pages 183-184, 2014.
20. The GPy authors. GPy: A gaussian process framework in python. http://github.com/SheffieldML/GPy, 2012-2014.
21. T Togawa. Home health monitoring. Journal of medical and dental sciences, 45(3):151-160, September 1998.
22. Xinpeng Zhang, T. Kato, and T. Matsuyama. Learning a context-aware personal model of appliance usage patterns in smart home. In IEEE Innovative Smart Grid Technologies - Asia, pages 73-78, Kuala Lumpur, Malaysia, 2014.
23. Mingjun Zhong, Nigel Goddard, and Charles Sutton. Signal aggregate constraints in additive factorial hmms, with application to energy disaggregation. In Advances in Neural Information Processing Systems, pages 3590-3598, 2014.
24. T Zia, D Bruckner, and A Zaidi. A hidden Markov model based procedure for identifying household electric loads. In 37th IEEE Annual Conference on Industrial Electronics Society, pages 3218-3223, 2011.
25. Jean-Paul Zimmermann, Matt Evans, Jonathan Griggs, Nicola King, Les Harding, Penelope Roberts, and Chris Evans. Household Electricity Survey A study of domestic electrical product usage. Technical report, Intertek, 2012.
26. A Zoha, A Gluhak, M Nati, and M A Imran. Low-power appliance monitoring using Factorial Hidden Markov Models. In Eighth IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pages 527-532, 2013.