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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition.

William Van Woensel¹, Samina Abidi¹, Syed Sibte Raza Abidi¹

¹NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.

Studies in Health Technology and Informatics

|August 24, 2019

Summary

This summary is machine-generated.

This study introduces a new knowledge-driven approach for activity recognition in Assisted Ambient Living (AAL). It accurately identifies correct and incorrect daily living activities for individuals with cognitive decline.

Keywords:

Activities of Daily Living Artificial Intelligence

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Area of Science:

Human-Computer Interaction
Artificial Intelligence
Gerontology

Background:

Assisted Ambient Living (AAL) aims to enhance self-sufficiency for individuals with disabilities through smart environments.
Dynamic guidance for Activities of Daily Living (ADL) is crucial for patients with cognitive decline.
Accurate activity recognition is essential for detecting ADL initiation and execution.

Purpose of the Study:

To develop an activity recognition system capable of identifying both correct and incorrect ADLs in cognitively impaired individuals.
To address the limitations of current systems that fail to detect erroneous actions (e.g., wrong order, location, objects).
To accommodate variations in correct ADL performance within smart environments.

Main Methods:

A novel knowledge-driven activity recognition approach is proposed.
Semantic reasoning is employed to interpret ADL workflows and environmental context.
The system recognizes correct, incorrect, and contextually varied ADL executions.

Main Results:

The developed approach successfully recognizes correct ADL performance.
It can identify deviations from the standard ADL workflow, including out-of-order, misplaced, or misused objects.
The system demonstrates robustness in handling non-uniform but correct ways of performing ADLs.

Conclusions:

The knowledge-driven, semantic reasoning approach enhances activity recognition for AAL.
This method provides critical insights into ADL execution for individuals with cognitive decline.
It supports safer and more effective assisted living by detecting errors and variations.