Visualisation to Explain Personal Health Trends in Smart Homes

TL;DR

This paper presents a visualization workflow that makes smart home health data more transparent and interpretable, aiming to enhance trust in AI-driven health recommendations through clear, color-coded graphs.

Contribution

It introduces a novel visualization method to improve interpretability and trust in AI systems analyzing smart home health data.

Findings

Enhanced transparency of health data analysis

Improved user trust through visual explanations

Clear, color-coded graphical representations

Abstract

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known "risky" profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs.