The Use of Hand Gestures as a Tool for Presentation

Or hands play a crucial role in daily activities, serving as a primary tool for interacting with technology. This paper explores using hand gestures to control presentations, offering a dynamic alternative to traditional devices like mice or keyboards. These conventional methods often limit presenters to a fixed position and depend on the device's proximity. In contrast, hand gesture controls promise a more fluid and engaging presentation style. This study utilizes the HaGRID dataset, supplemented by custom-recorded data, divided into 80% for training, and 10% each for validation and testing. The data undergoes preprocessing and a linear classifier with four dense layers and a SoftMax activation layer is employed. The model, optimized with the Adam optimizer and a learning rate of 1e-1, incorporates a motion classifier (LSTM) with two dense layers and an LSTM layer, tailored for long-distance body pose estimation. The resulting application, a local desktop tool independent of internet connectivity, uses tkinter for its user interface. It demonstrates high accuracy in classifying gestures, achieving 90.1%, 89%, and 90% in training, validation, and testing, respectively, for the linear classifier. The motion classifier records 79.8%, 72%, and 70.1%. The model effectively recognizes and categorizes dataset gestures, capturing live camera feeds to manage presentations. Users benefit from various features, including PowerPoint selection, distance mode, gesture toggling and assignment, and appearance mode. This study illustrates how hand gesture control can enhance presentation experiences, merging technology with natural human movement for a more seamless interaction.