Comparative study of vision-based and hall effect sensor systems for hand gesture recognition in human-computer interaction

Hand gesture recognition has emerged as a crucial technology for bridging communication gaps, particularly for individuals with hearing and speech impairments. This paper presents a detailed comparative study between two distinct approaches such as computer vision-based gesture recognition and Hall Effect sensor-based wearable gloves. While computer vision systems offer contactless interaction through camera-based detection, Hall Effect sensor gloves provide direct finger movement tracking through magnetic field variations. Our research evaluates both systems across multiple parameters including accuracy, usability, cost-effectiveness, and real-world applicability. Through practical implementation and testing, we demonstrate that vision-based systems achieve 92.1% accuracy compared to 86.9% for sensor-based gloves. However, each approach presents unique advantages depending on the application context. This comparative analysis provides valuable insights for researchers and developers working on assistive communication technologies.