Hand Sign Virtual Reality Data Processing Using Padding Technique

This study focuses on addressing the challenges of processing hand sign data in Virtual Reality environments, particularly the variability in data length during gesture recording. To optimize machine learning models for gesture recognition, various padding techniques were implemented. The data was gathered using the Meta Quest 2 device, consisting of 1,000 samples representing 10 American Sign Language hand sign movements. The research applied different padding techniques, including pre- and post-zero padding as well as replication padding, to standardize sequence lengths. Long Short-Term Memory networks were utilized for modeling, with the data split into 80% for training and 20% for validation. An additional 100 unseen samples were used for testing. Among the techniques, pre-replication padding produced the best results in terms of accuracy, precision, recall, and F1 score on the test dataset. Both pre- and post-zero padding also demonstrated strong performance but were outperformed by replication padding. This study highlights the importance of padding techniques in optimizing the accuracy and generalizability of machine learning models for hand sign recognition in Virtual Reality. The findings offer valuable insights for developing more robust and efficient gesture recognition systems in interactive Virtual Reality environments, enhancing user experiences and system reliability. Future work could explore extending these techniques to other Virtual Reality interactions.