An Interactive Human-in-the-Loop Framework for Skeleton-Based Posture Recognition in Model Education

TL;DR

This paper introduces an interactive framework using skeleton data to recognize postures, aiding in model training and art education with a Transformer model achieving 92.7% accuracy.

Contribution

A novel human-in-the-loop framework combining posture recognition with visual recommendations for educational applications.

Findings

Deep learning models outperform traditional ones on complex postures.

The Transformer model achieved 92.7% accuracy in posture classification.

The system enhances teaching efficiency through semi-automated feedback.

Abstract

This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: standing, sitting, jumping, crouching, and lying. From each image, comprehensive skeletal features are extracted, including joint coordinates, angles, limb lengths, and symmetry metrics. Multiple classification algorithms—traditional (KNN, SVM, Random Forest) and deep learning-based (LSTM, Transformer)—are compared to identify effective combinations of features and models. Experimental results show that deep learning models achieve superior accuracy on complex postures, while traditional models remain competitive with low-dimensional features. Beyond classification, the system integrates posture recognition with a visual recommendation module. Recognized poses are used to retrieve matched examples from a reference library, allowing instructors to browse and select posture suggestions for learners. This semi-automated feedback loop enhances teaching interactivity and efficiency. Among all evaluated methods, the Transformer model achieved the best accuracy of 92.7% on the dataset, demonstrating the effectiveness of our closed-loop framework in supporting pose classification and model training. The proposed framework contributes both algorithmic insights and a novel application design for posture-driven educational support systems.

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