Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). CFEB enables the Transformer model to leverage both local and global time series features for activity classification. Meanwhile, the absolute position embedding (APE) in existing Transformer models cannot accurately represent the distance relationship between individuals at different time points. To further explore positional correlations in temporal signals, this paper introduces the Vector-based Relative Position Embedding (vRPE), aiming to provide more relative temporal position information within sensor signals for the Transformer model. Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR.