A Personalized Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes

There is an urgent need to provide quality-of-life to a growing population of older adults living independently. Solutions that focus on the person and take into account their preferences and context are recognised as key. We introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and data from participants derived from semi-structured interviews, home layouts and additional contextual information, such as the researchers’ observations. These data are used to create formal models, personalised for each participant according to their preferences and context. Requirements specific to each individual are formulated and encoded in Linear Temporal Logic, and a model checker is used to verify whether each is satisfied by the model of the participant’s behaviour. We demonstrate the framework’s generalisability by applying it to two different participants, highlighting its potential to enhance the safety and well-being of older adults ageing in place.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-11-27
DOI
https://doi.org/10.1109/jsen.2025.3635781
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
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article

A Personalized Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes

Nuša Farič, Jacques Fleuriot, Jane Hillston, Filip Smola et al.
IEEE Sensors Journal
Context-Aware Activity Recognition Systems
article

A Personalized Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes

Nuša Farič, Jacques Fleuriot, Jane Hillston, Filip Smola, Juanjuan Zheng, Ricardo Contreras
article en

Abstract

There is an urgent need to provide quality-of-life to a growing population of older adults living independently. Solutions that focus on the person and take into account their preferences and context are recognised as key. We introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and data from participants derived from semi-structured interviews, home layouts and additional contextual information, such as the researchers’ observations. These data are used to create formal models, personalised for each participant according to their preferences and context. Requirements specific to each individual are formulated and encoded in Linear Temporal Logic, and a model checker is used to verify whether each is satisfied by the model of the participant’s behaviour. We demonstrate the framework’s generalisability by applying it to two different participants, highlighting its potential to enhance the safety and well-being of older adults ageing in place.

IEEE Sensors JournalVol. 26(2)
University of Exeter (GB), University of Edinburgh (GB)
Openalex Percentile: Top 62%
Context-Aware Activity Recognition Systems
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