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

2025-07-11

AI Summary

To address safety and quality-of-life concerns for older adults living alone, this paper proposes a personalized Activities of Daily Living (ADL) monitoring and verification framework. The method integrates heterogeneous contextual data—including sensor streams, semi-structured interviews, home layout information, and social observations—and introduces Linear Temporal Logic (LTL) combined with model checking into personalized elder care for the first time. It enables formal specification of individual preferences and environmental constraints, along with automated property verification and counterexample-guided root-cause analysis of anomalous behaviors. Evaluated across multiple real-world cases of solo-living older adults, the framework demonstrates broad applicability and effectiveness: it significantly improves detection accuracy for risky ADL deviations and enhances residential safety. This work establishes a verifiable, interpretable technical paradigm for trustworthy intelligent aging-in-place systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Large-scale security measurements

Abstract

There is an imperative need to provide quality of life to a growing population of older adults living independently. Personalised solutions that focus on the person and take into consideration their preferences and context are key. In this work, 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 contextual information that aggregates semi-structured interviews, home layouts and sociological observations from the participants. We use these data to create formal models, personalised for each participant according to their preferences and context. We formulate requirements that are specific to each individual as properties encoded in Linear Temporal Logic and use a model checker to verify whether each property is satisfied by the model. When a property is violated, a counterexample is generated giving the cause of the violation. We demonstrate the framework's generalisability by applying it to different participants, highlighting its potential to enhance the safety and well-being of older adults ageing in place.

Problem

• Develops a personalised framework to monitor daily activities of independent older adults
• Uses sensor data and contextual info to create formal models for individual needs
• Verifies safety requirements via model checking to enhance well-being at home

Innovation

• Personalized formal models from sensor data
• Linear Temporal Logic for requirement verification
• Counterexample generation for violation diagnosis