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Existing multimodal-based human action recognition approaches are computationally intensive, limiting their deployment in real-time applications. In this work, we present a novel and efficient pose-driven attention-guided multimodal network (EPAM-Net) for action recognition in videos. Specifically, we propose eXpand temporal Shift (X-ShiftNet) convolutional architectures for RGB and pose streams to capture spatio-temporal features from RGB videos and their skeleton sequences. The X-ShiftNet tackles the high computational cost of the 3D CNNs by integrating the Temporal Shift Module (TSM) into an efficient 2D CNN, enabling efficient spatiotemporal learning. Then skeleton features are utilized to guide the visual network stream, focusing on keyframes and their salient spatial regions using the proposed spatial–temporal attention block. Finally, the predictions of the two streams are fused for final classification. The experimental results show that our method, with a significant reduction in floating-point operations (FLOPs), outperforms and competes with the state-of-the-art methods on NTU RGB-D 60, NTU RGB-D 120, PKU-MMD, and Toyota SmartHome datasets. The proposed EPAM-Net provides up to a 72.8x reduction in FLOPs and up to a 48.6x reduction in the number of network parameters. The code will be available at https://github.com/ahmed-nady/Multimodal-ActionRecognition. 

In this work, we propose a novel method for assessing students’ behavioral engagement by representing student’s actions and their frequencies over an arbitrary time interval as a histogram of actions. This histogram and the student’s gaze are utilized as input to a classifier that determines whether the student is engaged or not. For action recognition, we use students’ skeletons to model their postures and upper body movements. To learn the dynamics of a student’s upper body, a 3D-CNN model is developed. The trained 3D-CNN model recognizes actions within every 2-minute video segment then these actions are used to build the histogram of actions. To evaluate the proposed framework, we build a dataset consisting of 1414 video segments annotated with 13 actions and 963 2-minute video segments annotated with two engagement levels. Experimental results indicate that student actions can be recognized with top-1 accuracy 86.32% and the proposed framework can capture the average engagement of the class with a 90% F1-score.