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<p>Accurate detection and classification of different physical exercise postures play a crucial role in monitoring fitness levels, preventing injuries, and personalizing workout routines. Traditional approaches using handcrafted feature extraction and shallow classifiers often suffer from low generalization and limited scalability. To address these limitations, this paper explores advanced deep learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN), capsule networks (CapsNet), gated recurrent units (GRU), and multilayer perceptron (MLP), along with hybrid architectures, to accurately classify exercise quality categories. The models were trained on sensor data collected from smart devices, capturing motion dynamics and postural changes. Among the evaluated models, Capsule Network achieved the highest accuracy of 0.99, followed by hybrid MLP with CNN and transformer model with 0.97, demonstrating superior capability in recognizing complex activity patterns. The results show that deep learning models can effectively identify different exercise postures with high precision and recall, paving the way for intelligent fitness monitoring systems. Future work includes optimizing the models for real-time applications and extending the system to include a wider range of physical activities.</p>

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Hybrid deep learning with attention mechanism for monitoring and classifying physical exercise postures using sensor data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Yogesh Kumar,
  • Kamini Girdhar,
  • Ashish Oberoi,
  • Apeksha Koul,
  • Marcin Woźniak,
  • Muhammad Fazal Ijaz

摘要

Accurate detection and classification of different physical exercise postures play a crucial role in monitoring fitness levels, preventing injuries, and personalizing workout routines. Traditional approaches using handcrafted feature extraction and shallow classifiers often suffer from low generalization and limited scalability. To address these limitations, this paper explores advanced deep learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN), capsule networks (CapsNet), gated recurrent units (GRU), and multilayer perceptron (MLP), along with hybrid architectures, to accurately classify exercise quality categories. The models were trained on sensor data collected from smart devices, capturing motion dynamics and postural changes. Among the evaluated models, Capsule Network achieved the highest accuracy of 0.99, followed by hybrid MLP with CNN and transformer model with 0.97, demonstrating superior capability in recognizing complex activity patterns. The results show that deep learning models can effectively identify different exercise postures with high precision and recall, paving the way for intelligent fitness monitoring systems. Future work includes optimizing the models for real-time applications and extending the system to include a wider range of physical activities.