Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure

Carl Chalmers*, William Hurst, Michael Mackay, Paul Fergus

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)


The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient’s condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of applications that are unachievable using the traditional energy grid. By 2020, UK energy suppliers will install 50 million smart meters, therefore, providing access to a highly accurate sensing network. Each smart meter records the electrical load for a given property at 30 minute intervals. This granular data captures detailed habits and routines through the occupant’s interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders.

Original languageEnglish
Pages (from-to)1154-1166
Number of pages13
JournalBehaviour and Information Technology
Issue number11
Publication statusPublished - 7 Feb 2019
Externally publishedYes


  • assistive technologies
  • early intervention practice
  • Health monitoring
  • machine learning
  • profiling
  • smart meters

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