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All Journal Jurnal Administrasi Kesehatan Indonesia Journal of Information Technology and Computer Engineering Journal of Electronics, Electromedical Engineering, and Medical Informatics
Shafaghi, Shadi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions

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Journal : journal of information technology and computer engineering

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UAV With the Ability to Control with Sign Language and Hand by Image Processing Hojaji, Hediyeh; Delisnav, Alireza; Ghafouri Moghaddam, Mohammad Hossein; Ghorbani, Fariba; Shafaghi, Shadi; Shafaghi, Masoud
JITCE (Journal of Information Technology and Computer Engineering) Vol 8 No 2 (2024): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.2.49-57.2024

Abstract

Automatic recognition of sign language from hand gesture images is crucial for enhancing human-robot interaction, especially in critical scenarios such as rescue operations. In this study, we employed a DJI TELLO drone equipped with advanced machine vision capabilities to recognize and classify sign language gestures accurately. We developed an experimental setup where the drone, integrated with state-of-the-art radio control systems and machine vision techniques, navigated through simulated disaster environments to interact with human subjects using sign language. Data collection involved capturing various hand gestures under various environmental conditions to train and validate our recognition algorithms, including implementing YOLO V5 alongside Python libraries with OpenCV. This setup enabled precise hand and body detection, allowing the drone to navigate and interact effectively. We assessed the system's performance by its ability to accurately recognize gestures in both controlled and complex, cluttered backgrounds. Additionally, we developed robust debris and damage-resistant shielding mechanisms to safeguard the drone's integrity. Our drone fleet also established a resilient communication network via Wi-Fi, ensuring uninterrupted data transmission even with connectivity disruptions. These findings underscore the potential of AI-driven drones to engage in natural conversational interactions with humans, thereby providing vital information to assist decision-making processes during emergencies. In conclusion, our approach promises to revolutionize the efficacy of rescue operations by facilitating rapid and accurate communication of critical information to rescue teams.
UAV With the Ability to Control with Sign Language and Hand by Image Processing Hojaji, Hediyeh; Delisnav, Alireza; Ghafouri Moghaddam, Mohammad Hossein; Ghorbani, Fariba; Shafaghi, Shadi; Shafaghi, Masoud
JITCE (Journal of Information Technology and Computer Engineering) Vol. 8 No. 2 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.2.49-57.2024

Abstract

Automatic recognition of sign language from hand gesture images is crucial for enhancing human-robot interaction, especially in critical scenarios such as rescue operations. In this study, we employed a DJI TELLO drone equipped with advanced machine vision capabilities to recognize and classify sign language gestures accurately. We developed an experimental setup where the drone, integrated with state-of-the-art radio control systems and machine vision techniques, navigated through simulated disaster environments to interact with human subjects using sign language. Data collection involved capturing various hand gestures under various environmental conditions to train and validate our recognition algorithms, including implementing YOLO V5 alongside Python libraries with OpenCV. This setup enabled precise hand and body detection, allowing the drone to navigate and interact effectively. We assessed the system's performance by its ability to accurately recognize gestures in both controlled and complex, cluttered backgrounds. Additionally, we developed robust debris and damage-resistant shielding mechanisms to safeguard the drone's integrity. Our drone fleet also established a resilient communication network via Wi-Fi, ensuring uninterrupted data transmission even with connectivity disruptions. These findings underscore the potential of AI-driven drones to engage in natural conversational interactions with humans, thereby providing vital information to assist decision-making processes during emergencies. In conclusion, our approach promises to revolutionize the efficacy of rescue operations by facilitating rapid and accurate communication of critical information to rescue teams.
Co-Authors Abolhassani, Moussa Bameri, Mohammad Hasan Bameri, Mohammad Mahdi Boshrabadi, Fatemeh Sadeghi Delisnav, Alireza Ghafouri Moghaddam, Mohammad Hossein Ghorbani, Fariba Hojaji, Hediyeh Shafaghi, Masoud