Leveraging Multihead Attention and Counterfactual Explanations for Precise and Efficient Activity Recognition and Heart Attack Detection

heart attack detection (HAD) and human activity recognition (HAR) rely on wearable sensor data to track heart health and physical activity in real-time, facilitating early detection and monitoring of health issues. However, existing solutions for HAR and HAD face challenges in effectively capturing spatial features, long-term dependencies, and diverse sensor data representations. These shortcomings impact recognition accuracy (ACC), memory efficiency, and processing speed, while also demanding substantial computational resources due to their complexity. They also lead to performance degradation, increasing the risk of inaccurate diagnoses and potentially jeopardizing patient lives. To overcome these limitations, a novel lightweight hybrid architecture for HAR and HAD is proposed, leveraging convolutional neural networks (CNNs) with gated recurrent units (GRUs) and multihead attention (MHA). CNNs capture spatial features, GRUs extract long-term dependencies, and MHA computes attention weights across data segments to highlight the most relevant features for health diagnosis, ensuring both improved performance and practicality for real-time health monitoring. Moreover, a magnitude-based weight pruning technique is adapted to reduce the proposed architecture’s complexity, making it suitable in resource-constrained settings without sacrificing ACC. Furthermore, our methodology integrates an optimized genetic algorithm for counterfactual explanations, recommending minimal health data changes to lower heart attack risk. Experimental results on a real testbed and datasets, including PAMAP2, WISDM, and Cleveland, demonstrate that the proposed method outperforms the state-of-the-art methods, achieving up to 3% improvement in F1-score and ACC, while reducing inference time, number of parameters, and memory footprint by over 40%, 70%, and 60%, respectively.