Enhancing Health Care Systems with Deep Learning-Based Hand Gesture Recognition

Abstract

Recent years have seen the integration of advanced technologies in healthcare, leading to innovative solutions for improving patient care and streamlining medical processes. One such innovation is the implementation of hand gesture recognition systems using deep learning. This paper explores the development and application of a hand gesture recognition system specifically designed for healthcare environments. Leveraging deep learning algorithms, the system accurately interprets hand gestures, enabling touchless interaction with medical devices and enhancing healthcare efficiency. Convolutional neural networks (CNNs) process and classify hand gestures captured through real-time video feeds. A comprehensive dataset of medical gestures was used to train the neural network, ensuring high precision and reliability. The system includes a robust preprocessing pipeline to handle variations in lighting, background, and hand orientation, improving performance in diverse clinical settings. Evaluation demonstrated significant accuracy in recognizing a wide range of gestures, outperforming traditional machine learning approaches. This touchless method reduces contamination risk in sterile environments and enhances accessibility for healthcare professionals, especially in critical situations. The successful integration of deep learning-based hand gesture recognition into healthcare systems represents a significant advancement in health technology, promising improved patient outcomes and streamlined processes.