A Unified Approach for Real-Time Human Activity Recognition in Wearable Devices Using Attention-Gated Spatiotemporal Fusion Networks and Optimized Sensor Data Processing

The advent of wearable sensor technologies has transformed sports science and personal health monitoring, but real-time Human Activity Recognition (HAR) remains a challenge, especially in dynamic environments. Existing approaches often struggle to adapt to rapid activity transitions, handle multimodal sensor noise, and optimize for computational efficiency on resource-constrained wearable devices. In this work, we introduce an innovative system that overcomes these limitations through a novel integration of deep learning models and sensor fusion techniques. At its core, our framework features the Attention-Gated Spatiotemporal Fusion Network (AG-SFN), a deep neural network that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with a unique attention mechanism to dynamically prioritize the most relevant features from multimodal time-series data for accurate activity classification. To address sensor noise and improve data integrity, we propose the Adaptive Multimodal Kalman Fusion (AMKF) algorithm, which adapts the filtering process based on the motion intensity and sensor variance in real time. To ensure the efficient deployment of this model on wearable devices, we introduce a Dynamic Channel Pruning with Knowledge Distillation (DCP-KD) method, which significantly reduces the model size and computational load by pruning less relevant features and distilling knowledge from a large teacher model to a smaller student model. Our system was rigorously evaluated on a custom dataset of 40 participants performing six distinct sport-related activities, achieving a state-of-the-art activity recognition accuracy of 98.6%, outperforming conventional models by a significant margin. The system also demonstrated exceptional real-time performance with heart rate monitoring error reduced to 0.85 beats per minute (bpm), latency under 90 ms, and a 35% reduction in energy consumption. This research establishes a new benchmark for the development of efficient and adaptive wearable systems for real-time sports health monitoring, offering a robust solution for next-generation wearable technologies with practical applications in sports, fitness, and healthcare.