BIOMEDICAL GLOVE SYSTEM FOR TRANSLATING HAND GESTURES INTO TEXT USING ARDUINO
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Abstract
This project represents an innovative practical step toward facilitating communication for individuals with hearing and speech impairments, by designing and implementing a device that uses tilt sensors and Arduino technology to convert finger gestures into written text. The results demonstrated that the device features high response speed and an acceptable accuracy rate of approximately 90%, combined with ease of use and low cost compared to other complex systems. Despite challenges such as sensitivity to rapid movements or improper glove fitting, the prototype proved its efficiency and potential for further development. This work opens promising horizons for future improvements, including expanding the set of recognizable gestures and integrating smart features such as wireless connectivity and text-to-speech functionality. Thus, the project highlights the importance of utilizing simple technologies to serve special needs groups and reinforces the concept of human- centered innovation within scientific and engineering research.
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[1] R. Kaur and P. Vig, "Assistive Technology for Communication Disorders: A Review of Sign Language Recognition Systems," International Journal of Computer Applications, vol. 178, no. 43, 2019. doi: 10.5120/ijca2019918805 (https://doi.org/10.5120/ijca2019918805).
[2] T.K. Bhattacharyya, A. Sharma, and P. Saha, "Talking Glove for Speech Impaired," International Journal of Engineering Research and General Science, vol. 2, no. 1, pp. 196–202, Jan 2014.
[3] S. Starner, J. Weaver, and A. Pentland, "Real-Time American Sign Language Recognition Using Desk and Wearable Computer-Based Video," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1371–1375, Dec. 1998. doi: 10.1109/34.735811 (https://doi.org/10.1109/34.735811).
[4] J. Kadous, "Machine Recognition of Auslan Signs Using PowerGloves: Towards Large-Lexicon Recognition of Sign Language," Proceedings of the Workshop on the Integration of Gesture in Language and Speech, pp. 165–174, 1998.
[5] O. Koller, H. Ney, and R. Bowden, "Deep Learning of Mouth Shapes for Sign Language," Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 2015. doi: 10.1109/ICCVW.2015.120 (https://doi.org/10.1109/ICCVW.2015.120).
[6] C. Wilson and M. Bobick, "Recognition and Interpretation of Parametric Gesture," Proceedings of the IEEE International Conference on Computer Vision, pp. 329–336, 1995. doi: 10.1109/ICCV.1995.466892 (https://doi.org/10.1109/ICCV.1995.466892).
[7] H. Kadry, A. Ezzat, and M. R. Mokhtar, "Smart Glove for Sign Language Recognition Using Flex Sensors," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 7, no. 12, pp. 369–374, 2016. Doi 10.14569/IJACSA.2016.071250 (https://doi.org/10.14569/IJACSA.2016.071250).
[8] A. M. Zadeh, T. Baltrušaitis, and L.-P. Morency, "Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection," Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 2017. doi: 10.1109/ICCVW.2017.152 (https://doi.org/10.1109/ICCVW.2017.152).
[9] Arduino Official Website, "Arduino Uno Rev3," [Online]. Available: https://store.arduino.cc/products/arduino-uno-rev3. [Accessed: 25-Apr- 2025].
[10] M. AlZain and A. Al-Harbi, "Survey on Sign Language Recognition Using Machine Learning Techniques," International Journal of Computer Applications, vol. 182, no. 24, 2019. doi: 10.5120/ijca2019918717 (https://doi.org/10.5120/ijca2019918717.