Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
This work addresses the need for real-time and resource-efficient fitness activity recognition for health monitoring and personalized training, though it is incremental as it combines existing methods.
The paper tackled the problem of fitness movement recognition by developing a lightweight framework that integrates pre-trained 2D CNNs and Vision Transformers with an LSTM enhanced by spatial attention, achieving a peak accuracy of 93.34% on a UCF101 subset.
Fitness movement recognition, a focused subdomain of human activity recognition (HAR), plays a vital role in health monitoring, rehabilitation, and personalized fitness training by enabling automated exercise classification from video data. However, many existing deep learning approaches rely on computationally intensive 3D models, limiting their feasibility in real-time or resource-constrained settings. In this paper, we present a lightweight and effective framework that integrates pre-trained 2D Convolutional Neural Networks (CNNs) such as ResNet50, EfficientNet, and Vision Transformers (ViT) with a Long Short-Term Memory (LSTM) network enhanced by spatial attention. These models efficiently extract spatial features while the LSTM captures temporal dependencies, and the attention mechanism emphasizes informative segments. We evaluate the framework on a curated subset of the UCF101 dataset, achieving a peak accuracy of 93.34\% with the ResNet50-based configuration. Comparative results demonstrate the superiority of our approach over several state-of-the-art HAR systems. The proposed method offers a scalable and real-time-capable solution for fitness activity recognition with broader applications in vision-based health and activity monitoring.