IDEALI: intuitively localising connected devices in order to support autonomy

TL;DR

This paper presents a novel approach for localising connected devices using semantic descriptions, aiding visually or cognitively impaired individuals, with algorithms evaluated on real-world testbeds showing high convergence rates.

Contribution

Introduction of a Semantic Position Description model and algorithms for transforming raw distance data into meaningful location descriptions for assistive technology.

Findings

Algorithms achieve up to 90% convergence with human judgments.

Evaluation conducted on real IoT testbed and human participants.

Demonstrates potential for improving device localisation in health and autonomy contexts.

Abstract

The ability to localise a smart device is very useful to visually or cognitively impaired people. Localisation-capable technologies are becoming more readily available as off-the-shelf components. In this paper, we highlight the need for such a service in the field of health and autonomy, especially for disabled people. We introduce a model for Semantic Position Description (SPD) (e.g. "The pill organiser in on the kitchen table") as well as various algorithms that transform raw distance estimations to SPD related to proximity, alignment and room identification. Two of these algorithms are evaluated using the LocURa4IoT testbed. The results are compared to the output of a pre-experiment involving ten human participants in the Maison Intelligente de Blagnac. The two studies indicate that both approaches converge up to 90% of the time. .