To address the issues of occlusion interference and low accuracy in gesture recognition under monocular vision

a gesture recognition method that integrates multimodal data from 2D skeletal keypoints

hand joint angles

and finger curvature is proposed. To achieve data fusion

a data acquisition system based on a bending sensor data glove and a monocular camera is constructed

and a dataset of gesture actions(including occlusion scene)is collected from seven subjects. By extracting hand keypoints and calculating joint angles

the skeletal

joint angle

and curvature information are concatenated into a multimodal input. Then

a convolutional neural networkbidirectional long short-term memory (CNN-BiLSTM) hybrid network is used to learn spatial and temporal features

respectively. Experimental results show that the proposed multimodal fusion method significantly improves the recognition accuracy from 68.34% to 84.13% compared to using only skeletal information

demonstrating that fusing finger curvature and joint angles effectively overcomes the occlusion problems and improves the robustness and accuracy of gesture recognition.