An Innovative Approach for Human Activity Recognition Based on a Multi-Head Attention Mechanism

Human activity recognition (HAR) leverages data from wearable devices and smartphones to detect actions, improving quality of life in areas like elderly care, health monitoring, and sports training. Current deep learning architectures struggle with extracting spatial features, long-term dependencies, and diverse sensor data representations, impacting recognition performance and posing challenges for resource-constrained IoT devices due to their complexity and parameter count. We propose a novel hybrid HAR architecture, integrating convolutional neural networks (CNN) and gated recurrent units (GRU) with a multi-head attention (MHA) mechanism. This architecture captures spatial features via CNN, extracts long-range dependencies with GRU, and uses MHA to compute attention weights for different data segments. The combined spatial and attention features are fed into a classification module for activity recognition. On the PAMAP2 dataset, our CNN-GRU-MHA model outperforms existing methods, achieving an F1-score of 98.4 %, with an inference time of 0.078 seconds and a memory footprint of 790.02 KB, reducing resource usage by 74.34 % and 62.81 %, respectively.