This study introduces an innovative, non-contact, and privacy-preserving hand gesture recognition system that leverages X-band and S-band radar technology and machine learning algorithms.
This study integrates transformer-based machine-learning models for real-time hand gesture recognition, using hand-tracking data captured through the OpenXR standard in Unity, and outlines how the approach can be extended toward detecting the flow of movement, i.e., the transitions between gestures, as future work.
Hand gesture recognition has been identified as an important field of research in the healthcare sector, specifically for applications in rehabilitation, assistive communication, and human computer interaction. Upper limb motion tracking provides useful information when determining the recovery of movement in patients...
Neela Harish, Deepa S., Umasankar Loganathan et al.· Journal of Innovative Image...· 1 citation
Despite their incredible desired properties of invariance, reliability and robustness, directional optical flow-based features still do not receive the attention they deserve in gesture recognition literature. Moreover, robust human hand pose estimation remains one of the most challenging tasks, because of its inherent...
Hand gesture recognition is a key enabling technology in vision-based touchless interaction systems, allowing users to interact with digital applications through natural hand movements without physical contact. Traditional input devices such as keyboards, mouse, and touchscreens present significant limitations in envir...
Avlin Antony, Jiss Kuruvilla, Christina Baiju et al.· International Conference Inn...· 0 citations
Hand gestures are a natural and expressive modality for human communication and are increasingly used in multimodal interaction, including human-computer interaction, extended reality, robotics, assistive technologies, and embodied AI. However, designing robust gesture-based interfaces remains challenging due to issues...
A. Giachetti, Marco Emporio, H. Wannous et al.· Proceedings of the 28th Inte...· 0 citations
Traditional machine learning methods continue to be a preferred approach in this context and the developed system has been demonstrated to achieve a detection rate of hand movements that exceeds 90% accuracy.
İsmail Yildiz, Ibrahim Seflek· Konya Journal of Engineering...· 0 citations
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