InertiaMPDD：用情绪惯性建模做多模态抑郁症检测
InertiaMPDD: Multimodal Personalized Depression Detection via Emotional Inertia-Inspired Dynamics Modeling
抑郁检测这块大多只输出标签，这篇换了个思路：先学健康人的情绪动态，再看偏离多远，还给出三个可解释的动力学指标。五个队列的 F1 数据都摆出来了，连在哪个数据集上不好使也写了，做医疗 AI 或多模态的可以看看
一篇发布在 arXiv 的论文提出 InertiaMPDD，用情绪惯性（emotional inertia）概念建模多模态抑郁症筛查。该方法基于“情绪具有持续性”这一现象，用一个受世界模型启发的潜在预测器学习健康人的下一状态模式，再以偏离健康参考的程度来判断抑郁风险。
技术上，模型利用预训练多模态表征，从语音、视觉行为和语言三模态的观测历史与上下文进行预测，并生成三个参考衍生的情绪动力学描述子：参考偏离度、残差衰减率和 Jacobian 谱描述子，可作为独立筛查的代理生物标志物，也可用于个性化决策校正。实验覆盖四个数据集的五个队列，多模态融合在 D-Vlog 上达到 98.41% F1，在 LMVD 上达到 84.98% F1。
值得注意的是，独立描述子在 MPDD 上优于固定加权融合，但在 AVEC2014 上区分能力较弱，说明参考信息的价值与融合收益是两回事，其效用依赖于具体数据集场景。这为抑郁症筛查研究提供了细粒度情绪动力学标注缺失问题的一种解决思路。
InertiaMPDD: Multimodal Personalized Depression Detection via Emotional Inertia-Inspired Dynamics Modeling
Multimodal depression screening estimates risk from speech, visual behavior and language. Current methods mainly predict labels, with limited evidence of departures from a non-depressed reference. Recording-level supervision also leaves fine-grained emotional dynamics unannotated. Motivated by emotional inertia, the persistence of affect, InertiaMPDD learns healthy next-state patterns using a world-model-inspired latent predictor. Using pretrained multimodal representations, it predicts from observed history and context. Reference deviation, residual decay rate and a Jacobian spectral descriptor form reference-derived emotional dynamics descriptors, evaluated as candidate proxy biomarkers for standalone screening and personalized decision correction. Across five cohorts from four datasets, fusion reaches 98.41% F1 on D-Vlog and 84.98% on LMVD. Standalone descriptors outperform fixed additive fusion on MPDD but discriminate weakly on AVEC2014. These findings distinguish reference informativeness from fusion benefit and reveal context-dependent utility.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include InertiaMPDD, 多模态, 抑郁症检测, D-Vlog.
Source attribution: curated from arXiv cs.AI (@Changzeng Fu, Songyang Wang, Chang Gao, Xinyi Zhang, Peilin Li, Mo Meng, Jiarui Wu, Mingze Li, Hongwei Ding, Shiqi Zhao, Yong Li, Lang He).