Gesture-Based Control of Presentation Slides using OpenCV

This research work proposes a Human Machine Interaction (HMI) system that enables users to control PowerPoint presentations using hand gestures. By analyzing hand movements captured by a camera and utilizing computer vision algorithms, the system accurately recognizes and interprets gestures as comm...

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Veröffentlicht in:2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) S. 1786 - 1791
Hauptverfasser: Vidya, M., Vineela, S., Sathish, P., Reddy, A. Supraja
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.08.2023
Schlagworte:
Cameras
Gesture recognition
Hand gestures
Human computer interaction
Human Machine Interaction
Keyboards
Machine learning
Machine learning algorithms
MediaPipe
OpenCV python
Personal digital devices
PyWin32
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Zusammenfassung:This research work proposes a Human Machine Interaction (HMI) system that enables users to control PowerPoint presentations using hand gestures. By analyzing hand movements captured by a camera and utilizing computer vision algorithms, the system accurately recognizes and interprets gestures as commands. The proposed system implementation incorporates machine learning techniques and the OpenCV module, allowing users to change slides without the need for keyboards or specialized gadgets. With real-time gesture recognition, users can control their presentations effortlessly and enhance accessibility. The system's potential applications include improving interaction with digital devices for individuals with limited access to traditional input devices.This proposed system highlights the significance of presentations and offers an innovative approach to nonverbal communication and human-computer interaction. By utilizing a camera and leveraging OpenCV Python, MediaPipe and PyWin32, the hand gesture presentation control system provides a user-friendly and efficient alternative for controlling PowerPoint slides. The system enhances the overall presentation experience by offering a more intuitive and natural means of interaction. The average accuracy of the proposed system is more than 93% for any distance from the camera.
DOI:10.1109/ICAISS58487.2023.10250520