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. 2022 Jan 4;22(1):371.
doi: 10.3390/s22010371.

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

Kiyoung Shin  1   2 , Ryan McConville  3 , Oussama Metatla  2 , Minhye Chang  1 , Chiyoung Han  4 , Junhaeng Lee  4 , Anne Roudaut  2
Affiliations

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

Kiyoung Shin et al. Sensors (Basel). .

Abstract

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.

Keywords: BLE RSSI; localization at an intersection; pedestrian navigation; visually impaired.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Examples of pushbutton-integrated accessible pedestrian signals from various manufacturers.
Figure 2
Figure 2
Communication block diagram of Crossing Assistance System (CAS).
Figure 3
Figure 3
Zone division for the feasibility study.
Figure 4
Figure 4
Data acquisition area is drawn by the GPS of the smartphone.
Figure 5
Figure 5
Confusion matrices for (a) raw data and (b) six points moving average of the Received Signal Strength Indicator (RSSI).
Figure 6
Figure 6
Flow chart of the method used to collect the data.
Figure 7
Figure 7
Nine zones for position classification.
Figure 8
Figure 8
Data sampling areas as illustrated by GPS.
Figure 9
Figure 9
Confusion matrix for the nine-zone classification task.
Figure 10
Figure 10
Example of CAS for pedestrian navigation after localization at the intersection.
See this image and copyright information in PMC

References

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