Communication is a fundamental human right, yet over 70 million individuals worldwide who are deaf or speech-impaired face daily barriers in expressing their needs in healthcare, education, workplaces, and emergencies. Sign language serves as the primary mode of communication for this population; however, it is not universally understood by the hearing community, creating a persistent and critical gap in accessibility. Existing assistive solutions such as camera-based AI translators require high computational power, stable internet connectivity, and controlled lighting conditions, making them unsuitable for portable, real-world deployment. This paper presents HandSpeak, a wearable embedded sign language translation system designed to bridge this communication divide affordably and reliably. The system integrates an Arduino Mega 2560 microcontroller, four flex sensors forming voltage-divider circuits on a glove, an SSD1306 OLED display for real-time text output, and an A7670C 4G GSM module for emergency SMS communication. The firmware, developed in Embedded C/C++ using Arduino IDE 2.x, continuously polls sensor inputs and maps gesture patterns to one of four predefined outputs: SICK, EMERGENCY, HELP, and WATER. Upon detection of the EMERGENCY gesture, the system automatically dispatches an alert SMS to five predefined contacts without requiring any internet connectivity. The entire system is fabricated at a cost of approximately INR 2,500–3,500, making it one of the most cost-effective embedded sign language translators reported in recent literature. Prototype testing validated 100% gesture recognition accuracy after per-sensor calibration, sub-150 ms display response, and reliable SMS delivery across all five emergency contacts.

Keywords - Sign Language Translation, Flex Sensors, Arduino Mega 2560, OLED Display, GSM Module, Gesture Recognition, Wearable Assistive Technology, Embedded Systems.

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1. Meshesha A, Fröschl U, Kebede M, Biratu TD, Worku Y, Hunduma F. Prevalence of hearing loss and factors associated with hearing loss in Ethiopia: findings from the 2023 National Ethiopia Hearing Survey. BMJ open. 2025 Jan 1;15(1):e086288.
2. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
3. Cassim MR, Parry J, Pantanowitz A, Rubin DM. Design and construction of a cost-effective, portable sign language to speech translator. Informatics in Medicine Unlocked. 2022 Jan 1;30:100927.
4. Hazari SS, Alam L, Al Goni N. Designing a sign language translation system using kinect motion sensor device. In2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) 2017 Feb 16 (pp. 344-349). IEEE.
5. Peleo DL, Patalay MM, Cruz JM, Ramirez EA. Sign the Voice: An Embedded Software for Voice to Sign Language Translation. In2024 2nd International Conference on Computing and Data Analytics (ICCDA) 2024 Nov 12 (pp. 1-6). IEEE.
6. Gonzalez H, Hernández S, Calderón O. Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence. International Journal of Online & Biomedical Engineering. 2024 Mar 1;20(3).
7. Mitra S, Acharya T. Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2007 Apr 16;37(3):311-24.
8. Premaratne P, Nguyen QJ. Consumer electronics control system based on hand gesture moment invariants. IET Computer vision. 2007 Mar 15;1(1):35-41.
9. Oz C, Leu MC. American sign language word recognition with a sensory glove using artificial neural networks. Engineering Applications of Artificial Intelligence. 2011 Oct 1;24(7):1204-13.
10. Rekimoto J. Gesturewrist and gesturepad: Unobtrusive wearable interaction devices. InProceedings Fifth International Symposium on Wearable Computers 2001 Oct 8 (pp. 21-27). IEEE.
11. Martin V. Design and implementation of a system for automatic sign language translation. InFuture Access Enablers of Ubiquitous and Intelligent Infrastructures 2015 Sep 23 (pp. 307-313). Cham: Springer International Publishing.
12. Kamal. Embedded Systems: Architechture, Programming and Design. McGraw-Hill Science/Engineering/Math; 2006 Sep 1.
13. Dipietro L, Sabatini AM, Dario P. A survey of glove-based systems and their applications. Ieee transactions on systems, man, and cybernetics, part c (applications and reviews). 2008 Jun 20;38(4):461-82.
14. Papatsimouli M, Kollias KF, Lazaridis L, Maraslidis G, Michailidis H, Sarigiannidis P, Fragulis GF. Real time sign language translation systems: a review study. In2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST) 2022 Jun 8 (pp. 1-4). IEEE.

Communication is a fundamental human right, yet over 70 million individuals worldwide who are deaf or speech-impaired face daily barriers in expressing their needs in healthcare, education, workplaces, and emergencies. Sign language serves as the primary mode of communication for this population; however, it is not universally understood by the hearing community, creating a persistent and critical gap in accessibility. Existing assistive solutions such as camera-based AI translators require high computational power, stable internet connectivity, and controlled lighting conditions, making them unsuitable for portable, real-world deployment. This paper presents HandSpeak, a wearable embedded sign language translation system designed to bridge this communication divide affordably and reliably. The system integrates an Arduino Mega 2560 microcontroller, four flex sensors forming voltage-divider circuits on a glove, an SSD1306 OLED display for real-time text output, and an A7670C 4G GSM module for emergency SMS communication. The firmware, developed in Embedded C/C++ using Arduino IDE 2.x, continuously polls sensor inputs and maps gesture patterns to one of four predefined outputs: SICK, EMERGENCY, HELP, and WATER. Upon detection of the EMERGENCY gesture, the system automatically dispatches an alert SMS to five predefined contacts without requiring any internet connectivity. The entire system is fabricated at a cost of approximately INR 2,500–3,500, making it one of the most cost-effective embedded sign language translators reported in recent literature. Prototype testing validated 100% gesture recognition accuracy after per-sensor calibration, sub-150 ms display response, and reliable SMS delivery across all five emergency contacts.

Keywords - Sign Language Translation, Flex Sensors, Arduino Mega 2560, OLED Display, GSM Module, Gesture Recognition, Wearable Assistive Technology, Embedded Systems.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Meshesha A, Fröschl U, Kebede M, Biratu TD, Worku Y, Hunduma F. Prevalence of hearing loss and factors associated with hearing loss in Ethiopia: findings from the 2023 National Ethiopia Hearing Survey. BMJ open. 2025 Jan 1;15(1):e086288.
2. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
3. Cassim MR, Parry J, Pantanowitz A, Rubin DM. Design and construction of a cost-effective, portable sign language to speech translator. Informatics in Medicine Unlocked. 2022 Jan 1;30:100927.
4. Hazari SS, Alam L, Al Goni N. Designing a sign language translation system using kinect motion sensor device. In2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) 2017 Feb 16 (pp. 344-349). IEEE.
5. Peleo DL, Patalay MM, Cruz JM, Ramirez EA. Sign the Voice: An Embedded Software for Voice to Sign Language Translation. In2024 2nd International Conference on Computing and Data Analytics (ICCDA) 2024 Nov 12 (pp. 1-6). IEEE.
6. Gonzalez H, Hernández S, Calderón O. Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence. International Journal of Online & Biomedical Engineering. 2024 Mar 1;20(3).
7. Mitra S, Acharya T. Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2007 Apr 16;37(3):311-24.
8. Premaratne P, Nguyen QJ. Consumer electronics control system based on hand gesture moment invariants. IET Computer vision. 2007 Mar 15;1(1):35-41.
9. Oz C, Leu MC. American sign language word recognition with a sensory glove using artificial neural networks. Engineering Applications of Artificial Intelligence. 2011 Oct 1;24(7):1204-13.
10. Rekimoto J. Gesturewrist and gesturepad: Unobtrusive wearable interaction devices. InProceedings Fifth International Symposium on Wearable Computers 2001 Oct 8 (pp. 21-27). IEEE.
11. Martin V. Design and implementation of a system for automatic sign language translation. InFuture Access Enablers of Ubiquitous and Intelligent Infrastructures 2015 Sep 23 (pp. 307-313). Cham: Springer International Publishing.
12. Kamal. Embedded Systems: Architechture, Programming and Design. McGraw-Hill Science/Engineering/Math; 2006 Sep 1.
13. Dipietro L, Sabatini AM, Dario P. A survey of glove-based systems and their applications. Ieee transactions on systems, man, and cybernetics, part c (applications and reviews). 2008 Jun 20;38(4):461-82.
14. Papatsimouli M, Kollias KF, Lazaridis L, Maraslidis G, Michailidis H, Sarigiannidis P, Fragulis GF. Real time sign language translation systems: a review study. In2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST) 2022 Jun 8 (pp. 1-4). IEEE.