ArticleSensors (Basel, Switzerland)2024
Real-Time Hand Gesture Monitoring Model Based on MediaPipe's Registerable System.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Emotional modulation of temporal density in natural grasping: a vision-based kinematic study.Frontiers in psychology · 2026Article
- Design and evaluation of a non-contact AI system for monitoring unplanned device removal in neurocritical care.BMC nursing · 2025Article
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Authors and funding
4 authors.
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Abstract
Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. It also improves ResNet and introduces FingerNet for registerable single-hand gesture recognition. The proposed model performs well on the RGDS dataset. The system is registerable, allowing users to flexibly register their own hand gestures for personalized gesture recognition.
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