Design and functional evaluation of adaptive clothing for female wheelchair users driven by deep learning

This is particularly vital in the design of personalized wearable systems, where traditional approaches often rely on static, rule-based mechanisms that fail to adapt to complex, real-world constraints. Conventional techniques in adaptive design often fail to capture subtle relationships and contextual patterns relevant to real-world user needs, leading to models that generalize poorly across diverse user settings. To address this limitation, we present an integrated framework that leverages a Transductive Relational Encoder Network (TREN) and a Contextual Curriculum Optimization Protocol (CCOP). TREN encodes structural relationships among inputs via graph-based attention and gated message passing, enabling nuanced representation learning aligned with inter-instance semantics. Meanwhile, CCOP orchestrates a stage-wise training schedule that adapts dynamically to input complexity, using entropy-guided curriculum selection and temporal coherence alignment to regularize learning. Together, these innovations create a robust pipeline that improves discriminative performance while preserving contextual sensitivity, offering significant enhancements in functional adaptability and user-centric inference. This methodological synergy aligns with the journal’s interdisciplinary focus on intelligent interfaces, adaptive algorithms, and personalized computing systems. Experimental results show that our method achieves superior performance compared to existing baselines, with an average mAP improvement of 2.5–4.0 points across four benchmark datasets. Specifically, our approach reached an mAP of 80.76 on the CAESAR dataset and 88.53 on UP-3D, surpassing YOLOv8 and DETR by notable margins. These results validate the model’s enhanced adaptability, accuracy, and user-centric inference capabilities.