AirSwap: Touchless File Transfer Through Hand Gestures

AirSwap is a gesture-driven touchless file-transfer platform designed to make it easy for users to share files between devices without requiring physical interaction or complicated communication methods. The project successfully connects gesture recognition and wireless communication through a simple and user-friendly system, offering features such as real-time hand tracking, gesture-based file transfer, wireless peer-to-peer communication, and interactive transfer monitoring. It helps users perform seamless file sharing with improved convenience, reduced operational complexity, and faster communication between devices. While the platform has certain limitations such as lighting dependency, webcam constraints, and network availability, it still provides a strong foundation for future improvements. With advanced gesture models, enhanced security, and wider platform compatibility, AirSwap can become an efficient, user-friendly, and highly impactful touchless communication platform for modern digital environments.

1.          K. Y. Chen, M. S. Tsai, D. S. Yang and H. Y. Chen, “AirLink: Sharing Files Between Multiple Devices Using In-Air Gestures,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 565–569, 2014.  2.          S. K. Vasudevan and C. Vivek, “An Intelligent Attempt to Export Files into Cloud in Handheld Devices through Gesture Recognition,” Indian Journal of Science and Technology, vol. 8, no. 35, pp. 1–6, 2015.   3.          X. Zhang, Y. Li and H. Wang, “Real-Time Hand Gesture Recognition Using Computer Vision Techniques,” IEEE Access, vol. 10, pp. 11234–11246, 2022, doi: 10.1109/ACCESS.2022.3145678.  4.          T. Kim and J. Lee, “Touchless Human–Computer Interaction Based on Hand Gesture Recognition,” International Journal of Human–Computer Studies, vol. 145, p. 102512, 2021, doi: 10.1016/j.ijhcs.2020.102512.  5.          Google Developers, “MediaPipe Hands Framework Documentation,”    Available: https://developers.google.com/mediapipe 6.          OpenCV Organization, “OpenCV: Open Source Computer Vision Library,” Available: https://opencv.org/  7.          Python Software Foundation, “Socket Programming in Python,”   Available:https://docs.python.org/3/library/socket.html  8.          H. Kukenovs, “HAGRID: Hand Gesture Recognition Dataset,” GitHub Repository, Available: https://github.com/hukenovs/hagrid  9.          S. Mitra and T. Acharya, “Gesture Recognition: A Survey,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 3, pp. 311–324, 2007, doi: 10.1109/TSMCC.2007.893280.  10.      IEEE Xplore, “Hand Gesture Recognition Systems for Human–Computer Interaction,” Available: https://ieeexplore.ieee.org/