Intelligent Luminaire based Real-time Indoor Positioning for Assisted Living

TL;DR

This paper evaluates the accuracy of a real-time indoor positioning system using intelligent luminaires with Bluetooth sensing, aimed at assisting older adults in residential environments, and explores its feasibility for mobile users.

Contribution

It introduces a configurable cyber-physical system with luminaires that replace lighting and enable real-time indoor localization for assisted living, assessing its accuracy for moving subjects.

Findings

Achieved room-level accuracy with stationary subjects using filtering.

Assessed real-time localization feasibility for moving subjects.

Demonstrated cost-effective integration with existing lighting infrastructure.

Abstract

This paper presents an experimental evaluation on the accuracy of indoor localisation. The research was carried out as part of a European Union project targeting the creation of ICT solutions for older adult care. Current expectation is that advances in technology will supplement the human workforce required for older adult care, improve their quality of life and decrease healthcare expenditure. The proposed approach is implemented in the form of a configurable cyber-physical system that enables indoor localization and monitoring of older adults living at home or in residential buildings. Hardware consists of custom developed luminaires with sensing, communication and processing capabilities. They replace the existing lighting infrastructure, do not look out of place and are cost effective. The luminaires record the strength of a Bluetooth signal emitted by a wearable device equipped by the monitored user. The system's software server uses trilateration to calculate the person's location based on known luminaire placement and recorded signal strengths. However, multipath fading caused by the presence of walls, furniture and other objects introduces localisation errors. Our previous experiments showed that room-level accuracy can be achieved using software-based filtering for a stationary subject. Our current objective is to assess system accuracy in the context of a moving subject, and ascertain whether room-level localization is feasible in real time.