Page 31 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
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ection of different shapes and materials by glasses for
blind and visually impaired

Urban Košale Pia Žnidaršicˇ Kristjan Stopar

Faculty of Electrical Faculty of Electrical Faculty of Electrical
Engineering and Computer Engineering and Computer Engineering and Computer

Science Science Science
University of Maribor University of Maribor University of Maribor

Maribor, Slovenia Maribor, Slovenia Maribor, Slovenia

urban.kosale@student.um.si pia.znidarsic@student.um.si kristjan.stopar@student.um.si

ABSTRACT 1. INTRODUCTION

Glasses for blind and visually Impaired were built to help Glasses for Blind and Visually Impaired is a device built to
blind and visually impaired to navigate around diverse range help blind and visually impaired to navigate around diverse
of obstacles. range of obstacles. This device consists of two parts. First
part is a head mounted sensor assembly whereas the second
In this study, we tested how well the glasses detect different is a haptic feedback device, worn as a belt. Detection of
everyday shapes and materials. The results are crucial for obstacles is performed by 10 VL53L1X Time of Flight (ToF)
further development, because this device has to able to de- sensors [1], whose ranging data is then processed with an
tect wide variety of shapes and materials in order to be safe on-board ESP-WROOM-32 microcontroller [2] and send via
and reliable for everyday usage. Bluetooth connection to the belt. The belt is equipped with
a second ESP-WROOM-32 on-board microcontroller, which
For this purpose, we set glasses on a stationary stand and po- interprets the data and presents it to the wearer with 15
inted them directly into the obstacle centre. Obstacles made vibration motors arranged in a square grid. The glasses are
of different materials were attached on a moving stand. The worn on the head, whereas the belt is attached around the
results showed that the sensors discriminate the shapes at stomach area.
the distances between 30 and 90 cm from the glasses. At the
distance of 60 cm the triangle was successfully discriminated The motivation behind this work is the desire for this de-
from circle and rectangle, whereas the latter two were not vice to work in as many circumstances as possible. Rapid
easy to discriminate. The second experiment showed that technological development brings new materials and shapes
plexi glass and glass present a substantial detection chal- in our everyday living space. If we add fast lifestyle to the
lenge. On the other hand, aluminium foil, white paper and mix, we get the need for a device that can reliably detect
micro polyester are easily detected. different shapes and materials in close to real time.

Performance of the device was already tested on specifically
designed polygon with 12 test subjects. The results are writ-
ten in the next section. Expanding on these results, in this
study, we focus on testing and quantifying device’s ability
to detect different shapes and materials.

Keywords 2. POLYGON TEST RESULTS

glasses for the blind and visually imapred, detection, sensor, Test took place in the main lobby of the Faculty of Electri-
obstacle, material, shape cal Engineering and Computer Science, Maribor. This area
was used because it is big and open so the glasses detected
the obstacles only. It also provided room lighting conditions
and flat surface. Polygon seen on Figure 1 consisted out
of 4 obstacles made out of polystyrene foam. They simula-
ted everyday objects such as tables, poles and doors. Fifth
obstacle which simulated hanging obstacle was tested indi-
vidually. Every participant had two attempts. Test focused
on the number of detected obstacles, number of steps and
time necessary to finish the walk through the polygon [3].

All participants detected the fifth, hanging obstacle, but
only one out of 12 participants detected an obstacle which
simulated the table. On average 2.8 ± 0.39 out of 4 obstacles
were detected in the first run and 2.92 ± 0.29 out of 4 in

StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.31-34 31
Koper, Slovenia, 10 October
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