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Our paper on understanding the ADL of older adults has been published by IEEE Sensors

    Our paper describing “A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes” has been published by the journal  IEEE Sensors. This describes a novel approach and framework integrating symbolic modelling and data from sensors for understanding the Activities of Daily Living (ADLs) of older adults living independently in their homes in Edinburgh and its neighbourhood.

    The work is part of the Integrated Technologies of Care workpackage of the Advanced Care Research Centre (ACRC).

    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.
    Print ISSN: 1530-437X
    Online ISSN: 1558-1748
    DOI: 10.1109/JSEN.2025.3635781
    More information available here.

    Our paper on differentiable Signal Temporal Logic for neurosymbolic AI has been published by LIPIcs

      GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

      Authors Mark Chevallier , Filip Smola , Richard Schmoetten , Jacques D. Fleuriot 

      Part of: Volume: 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)
      Series: Leibniz International Proceedings in Informatics (LIPIcs)
      Conference: International Symposium on Temporal Representation and Reasoning (TIME)

      New pre-print out on our qualitative study of older adults living with sensors at home

        Our paper on “Early experiences and views of older adults living with sensing technology at home: A qualitative study” is available as a pre-print here.

        This study explored older adults’ perceptions and lived experiences of sensing technologies integrated into their home environments. Using semi-structured interviews and in-home observations, we examined how older individuals interacted with motion, magnetic, and physiological sensors embedded in their everyday routines.

        It is a companion paper to our modelling paper currently available on arXiv as arXiv:2507.08701.

        James Vaughan passes his PhD viva

          Congratulations to James Vaughan who has passed his PhD viva with minor corrections. His thesis was on “Adaptable Latent Semantics for Automated Reasoning in Large Theories” and  his examiners were Alexander Bolotov (University of Westminster) and Paul Jackson (Edinburgh).

          Our new paper on chronic illnesses and depression featured in UKRI news

            Our new paper, Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression, has just been published in Nature Communications Medicine. The results have been highlighted by the MRC on the UKRI website and featured in its newsletter. See:

            UKRI News: https://www.ukri.org/news/multiple-chronic-illnesses-linked-to-higher-risk-of-depression
            Edinburgh University post: https://www.ed.ac.uk/news/multiple-chronic-illnesses-could-double-risk-of-depression

            and

            Full paper at: https://doi.org/10.1038/s43856-025-00825-7
            Code at: https://github.com/laurendelong21/clusterMed