Gesture Recognition and Interpretation through Wearable Sensors and Recurrent Neural Networks

Gesture recognition has emerged as a promising technology for enabling more natural and intuitive interactions between humans and computers. This paper will focus on the use of wearable and Recurrent Neural Networks to recognize and interpret gestures. Activities and guidance knowledge plays an important role in supporting various technologies and have has been extensively researched in the functioning of computer vision. Apart from classification relying on video data or image sequences, numerous gesture recognition methods incorporate supplementary sensors or tools, such as wearable sensors and specialized cameras, to enhance the precision and resilience of gesture recognition systems. An exemplar of this phenomenon approach that has shown promising results is the use of wearable sensors and recurrent neural networks. These wearable sensors, such as wristband cameras, provide data on hand trajectories, which can be analyzed and classified using recurrent neural networks. This combination of wearable sensors and recurrent neural networks has been demonstrated to achieve good results in recognizing and interpreting gestures in various scenarios, including sign language and fingerspelling data.