发表机构
Arizona State University; Syracuse University（亚利桑那州立大学; 雪城大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对阿片类药物使用障碍跨受试者渴求检测难题，提出RETRACE框架，通过双编码器结合韧性上下文实现轻量个性化，在多模态数据集上较基线提升7%。
AI 中文摘要
从可穿戴生理信号中检测阿片类药物渴求至关重要但颇具挑战性，有望为阿片类药物使用障碍（OUD）患者提供主动干预支持，该挑战在跨受试者评估场景下尤为突出，因为渴求具有主观性、异质性，且常与压力的生理反应相互纠缠。实证分析显示，压力会引发强烈且可重复的自主神经反应，而与渴求相关的信号则更弱、更稀疏，且大多嵌入在与压力相关的生理信号中。研究进一步发现，心理韧性（影响压力调节和渴求易感性）无法通过短期可穿戴窗口可靠观测，但可通过可复用的受试者级代理指标捕捉，包括压力后心率恢复和自传体记忆。基于这些发现，本文提出RETRACE，即一种用于跨受试者可穿戴生理信号渴求估计的韧性引导特质条件框架。RETRACE将渴求检测重新定义为特质条件生理解读：不再假设相同生理模式在不同个体间含义一致，而是利用与韧性相关的受试者上下文指导推理。技术上，RETRACE引入新颖的双编码器设计，将可泛化的压力生理信号与受试者特异性的渴求解读分离，结合冻结的压力预训练编码器和韧性条件渴求编码器，通过特征级门控和表示级融合实现轻量个性化，无需目标用户的渴求标签或针对每个用户的重新训练。在包含可穿戴生理信号、压力与渴求标注及自传体叙事的新型多模态OUD数据集上进行评估，在留一受试者（LOSO）设置下，RETRACE较最强基线实现了高达7%的绝对性能提升。
英文摘要
Detecting opioid craving from wearable physiological signals is critical yet difficult, with the potential to support proactive interventions for individuals with opioid use disorder (OUD). This challenge is especially pronounced under subject-independent evaluation because craving is subjective, heterogeneous, and often physiologically entangled with stress. Our empirical analysis shows that stress elicits strong and reproducible autonomic responses, while craving-related signals are weaker, sparse, and largely embedded within stress-related physiology. We further show that psychological resilience, which shapes stress regulation and craving vulnerability, is not reliably observable from short-term wearable windows, but can be captured through reusable subject-level proxies, including post-stress heart-rate recovery and autobiographical memory recall. Motivated by these findings, we introduce RETRACE, a resilience-guided trait-conditioned framework for subject-independent craving estimation from wearable physiology. RETRACE reframes craving detection as trait-conditioned physiological interpretation: rather than assuming the same physiological pattern has the same meaning across individuals, it uses resilience-related subject context to guide inference. Technically, RETRACE introduces a novel dual-encoder design that separates generalizable stress physiology from subject-specific craving interpretation. It combines a frozen stress-pretrained encoder with resilience-conditioned craving encoder, using feature-level gating and representation-level fusion to enable lightweight personalization without target-user craving labels or per-user retraining. We evaluate RETRACE on a novel multimodal OUD dataset containing wearable physiology, stress and craving annotations, and autobiographical narratives. Under LOSO setup, RETRACE achieves up to 7% absolute improvement over the strongest baseline.
