RingRT: recognizing air-writing on the palm through an IMU-integrated ring

Abstract Gesture recognition is a fundamental technology in nearly all Human-Computer Interaction (HCI) devices, drawing significant attention from engineers and researchers worldwide. Fine-grained air-writing recognition within a small spatial range remains a challenging yet crucial research topic. In this paper, we propose a method, namely RingRT, which uses an inertial measurement unit (IMU) integrated in the ring to identify character air-writing actions within an area as big as the palm of one’s hand. RingRT leverages the neural network that integrates both the Transformer architecture and the Focused Linear Attention mechanism, enabling it to effectively analyze 6-axis motion signals and accurately recognize air-written characters. In experiments, we collected 24,120 air-writing samples within a 6 cm $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 6 cm area, comprising 67 characters from 24 participants across three scenarios. RingRT achieved a user-independent accuracy of 90.08% in offline evaluation and maintained over 86% accuracy in real-world scenarios.