Harnessing Quantum Attention: A Hybrid Deep Network for Wearable Sensor-based Activity Recognition

Wearable healthcare sensors enabled Human Activity Recognition (HAR) to become foundational for real-time health monitoring together with individualized medical treatments. The proposed work brings forward QHAR-Net as an application of Quantum Hybrid Attention-Based Deep Network that boosts both recognition precision and operational speed. QHAR-Net achieves multi-modal sensor data pattern recognition through integrating the quantum feature encoding method with ResNet for spatial extraction along with LSTM for temporal dependencies and an attention mechanism. A complete evaluation of the model was performed using a Kaggle database containing thirty selections of ten different human activities. The research demonstrated QHAR-Net reaching 96.5% accuracy which surpassed both CNN-LSTM using 92.1% accuracy and ResNet-LSTM using 94.3% accuracy. Quantum and attention components demonstrated major roles in the overall model performance based on the ablation test. The real-time device testing produced quick inferences within 45 milliseconds using low system resources which indicates suitability for healthcare applications operating from mobile locations. The good results from QHAR-Net research demonstrate its potential to transform portable healthcare monitoring solutions by enhancing measurement precision and processing speed.