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Article

Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections

1
RSS Center, Korea Electrotechnology Research Institute, Ansan 15588, Korea
2
Department of Computer Science, University of Bristol, Bristol BS8 1TR, UK
3
Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, UK
4
Corporate Affiliated Research Institute, Human Care Co., Ltd., Ansan 15258, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Arturo de la Escalera Hueso
Sensors 2022, 22(1), 371; https://doi.org/10.3390/s22010371
Received: 17 November 2021 / Revised: 16 December 2021 / Accepted: 28 December 2021 / Published: 4 January 2022
(This article belongs to the Section Electronic Sensors)
One of the major challenges for blind and visually impaired (BVI) people is traveling safely to cross intersections on foot. Many countries are now generating audible signals at crossings for visually impaired people to help with this problem. However, these accessible pedestrian signals can result in confusion for visually impaired people as they do not know which signal must be interpreted for traveling multiple crosses in complex road architecture. To solve this problem, we propose an assistive system called CAS (Crossing Assistance System) which extends the principle of the BLE (Bluetooth Low Energy) RSSI (Received Signal Strength Indicator) signal for outdoor and indoor location tracking and overcomes the intrinsic limitation of outdoor noise to enable us to locate the user effectively. We installed the system on a real-world intersection and collected a set of data for demonstrating the feasibility of outdoor RSSI tracking in a series of two studies. In the first study, our goal was to show the feasibility of using outdoor RSSI on the localization of four zones. We used a k-nearest neighbors (kNN) method and showed it led to 99.8% accuracy. In the second study, we extended our work to a more complex setup with nine zones, evaluated both the kNN and an additional method, a Support Vector Machine (SVM) with various RSSI features for classification. We found that the SVM performed best using the RSSI average, standard deviation, median, interquartile range (IQR) of the RSSI over a 5 s window. The best method can localize people with 97.7% accuracy. We conclude this paper by discussing how our system can impact navigation for BVI users in outdoor and indoor setups and what are the implications of these findings on the design of both wearable and traffic assistive technology for blind pedestrian navigation. View Full-Text
Keywords: visually impaired; localization at an intersection; pedestrian navigation; BLE RSSI visually impaired; localization at an intersection; pedestrian navigation; BLE RSSI
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MDPI and ACS Style

Shin, K.; McConville, R.; Metatla, O.; Chang, M.; Han, C.; Lee, J.; Roudaut, A. Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections. Sensors 2022, 22, 371. https://doi.org/10.3390/s22010371

AMA Style

Shin K, McConville R, Metatla O, Chang M, Han C, Lee J, Roudaut A. Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections. Sensors. 2022; 22(1):371. https://doi.org/10.3390/s22010371

Chicago/Turabian Style

Shin, Kiyoung, Ryan McConville, Oussama Metatla, Minhye Chang, Chiyoung Han, Junhaeng Lee, and Anne Roudaut. 2022. "Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections" Sensors 22, no. 1: 371. https://doi.org/10.3390/s22010371

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