Adaptive deep reinforcement learning-based error analysis model for strength training

To tackle limitations of conventional strength training movement evaluation, such as inadequate movement stage segmentation and insufficient fine-grained error perception, this study proposes ARLE-Net.This framework integrates improved YOLOv8 with CBAM attention mechanism to enhance local movement feature detection, HR-Net for high-resolution human keypoint extraction, and adaptive reinforcement learning for dynamic action segmentation and standardised score prediction.Experiments on Fitness-AQA and ARFit datasets show it outperforms state-of-the-art methods in action segmentation accuracy and score prediction mean square error, with strong stability.Ablation studies confirm component effectiveness, providing support for intelligent fitness guidance and fitting the journal's enhanced ubiquitous computing focus.