This work proposes a novel HAR framework that integrates GCNs with an Adaptive Adjacency Matrix for spatial modeling and the Mamba State Space Model for temporal feature extraction, which achieves competitive performance with an approximately 72% reduction in parameter count.
Comprehensive ablation studies reveal that the architectural innovations and the stability protocol contribute complementarily, and that BPA-STGCN achieves the best accuracy–stability trade-off among all tested configurations.
A novel approach called Spatio-Temporal Local Graph Convolutional Network, which consists of a Partitioning Module (PM), a Spatio-Temporal Graph Convolution (ST-GC) module and a Joint Prototype Loss (JPL) function, which facilitates the capture of local relationships between joints.
Wen-Peng Wang, Guo-Long Qin, Xi-Zhan Gao et al.· International Journal of Mac...· 0 citations
This article proposes ActNet, a novel deep convolutional neural network architecture that combines multi-scale feature learning with a focus-aware attention mechanism to address the problem of recognizing human actions from still images.
A novel action recognition method, named MICA-Net, which combines data from multiple sensors to improve the efficiency of the HAR model, and a new compact version of a wrist-worn sensor device with Wi-Fi connectivity to an edge device, enhancing usability in human-machine interaction applications.
Trung-Hieu Le, Thai-Khanh Nguyen, T. Tran et al.· ACM Transactions on Multimed...· 0 citations
