LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Springer Verlag
Languages: English
Types: Unknown
Subjects: QA76
With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Department of Health, 'Report Long-term conditions compendium of Information: 3rd edition' (2013), Published to DH website.
    • [2] Margaret Chan, 'Dementia A public health priority, World Health Organization' (2012) [online] http://www.globalaging.org/agingwatch/Articles/Dementia%20a%20public%20 health%20priority.pdf (accessed 12/02/2016).
    • [3] Mental Health Foundation, 'The Fundamental Facts The latest facts and figures on mental health', [online] http://www.mentalhealth.org.uk/content/assets/PDF/publications/fundamental_f acts_2007.pdf?view=Standard (accessed 18/01/2016).
    • [4] Prince, M, Knapp, M, Guerchet, M, McCrone, P, Prina, M, Comas-Herrera, A, Wittenberg, R, Adelaja, B, Hu, B, King, D, Rehill, A & Salimkumar, D 2014, Dementia UK: Second Edition - Overview. Alzheimer's Society.
    • [5] Paul McCrone, Sujith Dhanasiri, Anita Patel, Martin Knapp and Simon LawtonSmith, PAYING THE PRICE The cost of mental health care in England to 2026, King's Fund.
    • [6] Mental Health Network NHS CONFEDERATION, Early intervention in psychosis services, 2011.
    • [7] M. Anas, N. Javaid, A. Mahmood, S. M. Raza, U. Qasim, Z. A. Khan 'Minimizing Electricity Theft using Smart Meters in AMI' Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2012.
    • [8] M. Popa. 'Data Collecting from Smart Meters in an Advanced Metering Infrastructure' INES 2011 15th International Conference on Intelligent Engineering Systems, June 2011, pp. 137 - 142.
    • [9] Coalton Bennett and Darren Highfill. Networking AMI Smart Meters, IEEE Energy2030, November 2008.
    • [10] Andr´es Molina-Markham, Prashant Shenoy, Kevin Fu, Emmanuel Cecchet, and David Irwin, (2010) 'Private Memoirs of a Smart Meter' BuildSys '10 Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 61-66.
    • [11] Lingfeng Wang, Devabhaktuni, V., Gudi, N., 'Smart Meters for Power Grid - Challenges, Issues, Advantages and Status' 2011 IEEE/PES Power Systems Conference and Exposition (PSCE), pp. 1-7, March 2011.
    • [12] Francesco Benzi, Norma Anglani, Ezio Bassi and Lucia Frosini Electricity Smart Meters Interfacing the Households, IEE transactions on Industrial Electronics, Vol 58, No10, VOL. 58, NO. 10, 2011.
    • [13] Eoghan McKenna, Ian Richardson, Murray Thomson, (2012) 'Smart meter data: Balancing consumer privacy concerns with legitimate applications' Energy Policy, VOL.41, pp. 807-814.
    • [14] Vojdani (2008) 'A Smart integration' IEEE Power & Energy Magazine, vol71 issue 9, pp. 71-79.
    • [15] Department of Energy and Climate Change 'Smart Meters, Smart Data, Smart Growth' (2015) [online] https://www.gov.uk/government/publications/smartmeters-smart-data-smart-growth (accessed 22/02/2016).
    • [16] Michael S. Mega, Jeffrey L. Cummings, Tara Fiorello, Jeffrey Gornbein 'The spectrum of behavioural changes in Alzheimer's disease' NEUROLOGY, 1996, VOL.46, pp. 130-135.
    • [17] Madhukar H Trivedi, (2004) 'The link between depression and physical symptoms' Primary care companion to the Journal of clinical psychiatry, VOL.6, pp. 12-16.
    • [18] Early Psychosis (2016) [online] http://www.earlypsychosis.ca/pages/curious/warning-signs-of-psychosis] accessed October 2015 (accessed 14/03/2016).
    • [19] Sandra Selikson, Karla Damus, David Hameramn (2015) 'Risk Factors Associated with Immobility' Journal of the American Geriatrics Society.
    • [20] Chiara Baglionia, Gemma Battaglieseb, Bernd Feigea, Kai Spiegelhaldera, Christoph Nissena, Ulrich Voderholzera, c, Caterina Lombardob, Dieter Riemanna, Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies, Journal of Affective Disorders, 2011.
    • [21] Hyong Jin Cho, Helen Lavretsky, Richard Olmstead, Myron J. Levin, Michael N. Oxman, Michael R. Irwin, Sleep Disturbance and Depression Recurrence in Community-Dwelling Older Adults: A Prospective Study, THE AMERICAN JOURNAL OF PSYCHIATRY, December 2008.
    • [22] N. Marom, L. Rokach, and A. Shmilovici, Using the Confusion Matrix for Improving Ensemble Classifiers, Proceedings of the Twenty-Sixth IEEE Convention of Electrical and Electronics Engineers in Israel, pp. 000555- 000559, 2010.
    • [23] Ahmed J. Aljaaf, Dhiya Al-Jumeily, Abir J. Hussain, Paul Fergus, Mohammed Al-Jumaily and Naeem Radi, Applied Machine Learning Classifiers for Medical Applications: Clarifying the behavioural patterns using a variety of datasets, Systems, Signals and Image Processing (IWSSIP), 2015 International Conference, September 2015.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article