To address the challenge that gesture recognition systems often suffer from limited recognition accuracy for complex micro-gestures due to insufficient extraction of fine-grained features such as fingertip motion trajectories and acceleration variations

a fine-grained recognition framework(FGRF)that integrates multi-dimensional feature analysis is proposed.By deploying dual-finger inertial measurement unit(IMU)sensors to capture raw signals

the framework constructs a highly discriminative feature set through joint time-frequency domain analysis.Principal component analysis(PCA)and a random forest(RF)classifier are employed to enable deep mining and efficient recognition of motion features.Experimental evaluations conducted on diverse datasets collected from 35 subjects demonstrate that the proposed method significantly outperforms mainstream machine learning and neural network models

achieving a recognition accuracy increased to 97.37%.