Sound-Based Localization Using LSTM Networks for Visually Impaired Navigation
Mohsen Bakouri,
Naif Alyami,
Ahmad Alassaf,
Mohamed Waly,
Tariq Alqahtani,
Ibrahim AlMohimeed,
Abdulrahman Alqahtani,
Md Samsuzzaman,
Husham Farouk Ismail,
Yousef Alharbi
Affiliations
Mohsen Bakouri
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Naif Alyami
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ahmad Alassaf
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Mohamed Waly
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Tariq Alqahtani
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ibrahim AlMohimeed
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Abdulrahman Alqahtani
Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Md Samsuzzaman
Department of Computer and Communication Engineering, Faculty of Computer Science and Engineering, Patuakhali Science and Technology, Patuakhali 6800, Bangladesh
Husham Farouk Ismail
Department of Biomedical Equipment Technology, Inaya Medical College, Riyadh 13541, Saudi Arabia
Yousef Alharbi
Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.
