Ying-Long Huang

Self-Supervised IMU-Based Human Activity Recognition with Deep Spatio-Temporal Feature Extraction and Adaptive Feature Fusion

The proposed SSL framework employs the Transformer and Depthwise Separable Convolution to jointly capture global temporal dependencies and local spatial features, which are adaptively fused into discriminative spatio-temporal representations for downstream HAR.