用轻量模型+注意力机制，提升健身动作识别准确率。
Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
- 结合预训练2D模型与带注意力的LSTM，兼顾空间与时间特征。
- 在UCF101子集上达到93.34%最高准确率。
- 适合实时健康监测、康复训练等资源受限场景。
健身动作识别是人体活动识别（HAR）的一个关键子领域，可实现视频数据中运动的自动分类，对健康监测、康复和个性化健身训练具有重要意义。然而，现有深度学习方法多依赖计算量大的3D模型，难以在实时或资源受限环境下应用。本文提出一种轻量高效框架，融合预训练的2D卷积神经网络（如ResNet50、EfficientNet、ViT）与增强型空间注意力机制的LSTM网络。这些模型高效提取空间特征，LSTM捕捉时间依赖性，注意力机制聚焦关键片段。在精选的UCF101数据子集上，基于ResNet50的配置达到93.34%的峰值准确率。对比实验表明，该方法优于多个先进HAR系统。所提方案具备可扩展性和实时处理能力，适用于视觉驱动的健康与活动监测。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。