Performance Evaluation of Deep Learning Techniques in Gesture Recognition Systems - Airlangga, Gregorius (Buletin Ilmiah Sarjana Teknik Elektro, 2024)
As human-computer interaction becomes increasingly sophisticated, the significance of gesture recognition systems has expanded, impacting diverse sectors such as healthcare, smart device interfacing, and immersive gaming. This study conducts an in-depth comparison of seven cutting-edge deep learning models to assess their capabilities in accurately recognizing gestures. The analysis encompasses Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), Simple Recurrent Neural Networks (RNNs), Multi-Layer Perceptrons (MLPs), Bidirectional LSTMs (BiLSTMs), and Temporal Convolutional Networks (TCNs). Evaluated on a dataset representative of varied human gestures, the models were rigorously scored based on accuracy, precision, recall, and F1 metrics, with LSTMs, GRUs, BiLSTMs, and TCNs outperforming others, achieving an impressive score bracket of 0.93 to 0.95. Conversely, MLPs trailed with scores around 0.59 to 0.60, underscoring the challenges of non-temporal models in processing sequential data. This study pinpoints model selection as pivotal for optimal system performance and suggests that recognizing the temporal patterns in gesture sequences is crucial. Limitations such as dataset diversity and computational demands were noted, emphasizing the need for further research into models' operational efficiencies. Future studies are poised to explore hybrid models and real-time processing, with the prospect of enhancing gesture recognition systems' interactivity and accessibility. This research thus provides a foundational benchmark for selecting and implementing the most suitable computational methods for advancing gesture recognition technologies.