Hybrid Positioning Approach for Context-Aware Indoor Navigation of Visually Impaired People

Abstract

Indoor localization for visually impaired persons is an open research problem, and individual localization approaches have failed to provide accurate results in the literature. Therefore, this paper presents a context-aware navigation approach for visually impaired persons using a hybrid localization approach. The proposed approach consists of four main modules, indoor localization, context detection, wayfinding, and feedback. The hybrid localization approach is based on the geomagnetic fields and Bluetooth low energy (BLE). Contextual information such as nearby locations and landmarks were obtained using geolocation-based augmented reality with a pre-deployed map and point of interest (POI). Voice and tactile were used to give feedback. Usability evaluation of the overall proof of concept (POC) prototype has shown promising results in context detection, wayfinding, and navigation. The average distance error of the hybrid localization algorithm has given an average 2.72 m distance error. Evaluation of the way findings give the average hit rate is 75.19%, and the miss rate is 24.81%. Usability evaluation results show that the proposed algorithm improves the context-awareness of the visually impaired indoor navigation.