EETANet: A Human-Centered Intelligent Framework for Exercise Posture Quality Assessment

As physical rehabilitation and fitness monitoring increasingly shift toward autonomous and home-based healthcare, accurate and intelligent posture assessment systems have become essential for supporting personalized rehabilitation and clinical decision making. This study proposes the Edge-Enhanced Temporal Attention Network (EETANet), a human-centered ambient intelligence framework for automated exercise posture assessment and quality evaluation. The proposed framework is designed as an end-to-end computational pipeline that transforms raw multivariate motion signals into posture quality predictions using a compact set of core biomechanical posture features. The pipeline integrates data preprocessing, signal enhancement, multi-domain feature representation, and deep neural classification to capture the relevant spatial and temporal characteristics of human movement. Signal preprocessing incorporates data cleaning, z-score normalization, and Savitzky-Golay smoothing to improve signal quality while preserving temporal dynamics. Subsequently, informative posture representations are extracted through complementary temporal, spectral, and morphological descriptors, enabling the model to characterize motion variations associated with posture quality. These representations are learned using a Multilayer Perceptron (MLP) classifier within the proposed EETANet framework to model nonlinear relationships between posture features and quality levels. Experimental evaluation demonstrates that the compact five-feature configuration achieves an accuracy of 90.83%, precision of 90.99%, recall of 90.83%, and F1-score of 90.84%. Notably, this compact configuration outperforms the corresponding 24-feature MLP configuration, which achieves 88.83% accuracy, indicating that a reduced set of core posture features can provide effective discriminative information while avoiding unnecessary feature redundancy. These findings demonstrate the potential of EETANet as an efficient framework for automated posture quality assessment in rehabilitation, fitness monitoring, and ambient healthcare applications.