发表机构
Telecom SudParis, Institut Polytechnique de Paris; School of Computer Science and Information Engineering, Chongqing Technology and Business University（巴黎电信学院，巴黎综合理工学院; 重庆工商大学计算机科学与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出NormPaST-Risk网络，通过多尺度编码和健康规范分支发现手写段级AD风险，在DARWIN基准上优于现有方法，并提供可解释证据。
AI 中文摘要
在线手写提供了一种非侵入性且低成本的阿尔茨海默病（AD）检测行为生物标志物，因为它同时反映了认知规划和精细运动控制。现有的基于手写的AD检测方法通常依赖全局轨迹特征或整体样本表示，这些方法可能受到个体书写风格、任务特定变化和采集噪声的强烈影响。在本文中，我们提出了NormPaST-Risk，一种健康规范性的Paper-Air选择性轨迹状态空间风险网络，用于从在线手写中进行可解释的AD检测。我们的方法不是将整个轨迹视为单一的整体表示，而是将AD手写检测重新定义为局部疾病相关段发现。具体来说，多尺度时间编码器在不同时间分辨率下捕捉笔划动态，而选择性Paper-Air状态空间编码器建模长期手写进展，并区分纸上运动执行与空中规划和过渡行为。为了明确表征异常偏差，健康规范性分支从健康对照中学习正常手写动态，而任务感知的多专家段风险模块估计段级AD风险，该风险通过隐藏状态变化和规范性偏差进行校准。弱监督的段级目标进一步实现了无需手动段注释的高风险段发现。在DARWIN基准上的实验表明，所提出的框架在AD/HC分类性能上优于现有方法。此外，发现的高风险段可以投影回原始手写轨迹，提供与AD相关手写变化相关的可解释证据。
英文摘要
Online handwriting provides a non-invasive and low-cost behavioral biomarker for Alzheimer's disease (AD) detection, as it reflects both cognitive planning and fine motor control. Existing handwriting-based AD detection methods usually rely on global trajectory features or whole-sample representations, which can be strongly affected by individual writing style, task-specific variation, and acquisition noise. In this paper, we propose NormPaST-Risk, a healthy-normative Paper-Air selective trajectory state-space risk network for interpretable AD detection from online handwriting. Instead of treating the entire trajectory as a single holistic representation, our method reformulates AD handwriting detection as local disease-relevant segment discovery. Specifically, a multi-scale temporal encoder captures stroke dynamics at different temporal resolutions, while a selective Paper-Air state-space encoder models long-range handwriting progression and distinguishes on-paper motor execution from in-air planning and transition behaviors. To explicitly characterize abnormal deviations, a healthy normative branch learns normal handwriting dynamics from healthy controls, and a task-aware multi-expert segment-risk module estimates segment-level AD risk calibrated by hidden-state changes and normative deviations. A weakly supervised segment-level objective further enables high-risk segment discovery without manual segment annotations. Experiments on the DARWIN benchmark demonstrate that the proposed framework achieves superior AD/HC classification performance compared with existing methods. Moreover, the discovered high-risk segments can be projected back to the original handwriting trajectory, providing interpretable evidence associated with AD-related handwriting variations.
