发表机构
University of Geneva; Stats-of-1; Boehringer Ingelheim Pharmaceuticals Inc.（日内瓦大学; Stats-of-1; 勃林格殷格翰制药公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对纵向情感计算的节奏不匹配问题，提出三种波级表征映射方式，利用Providemus研究数据建模21项情感认知结果，发现不同表征对情感和认知预测的效果差异，贡献了表征三元组等实用方案。
AI 中文摘要
纵向野外可穿戴传感可生成每日水平的生理、睡眠、活动及环境数据流，而情感与认知仅以周期性方式（按波次）标注。我们将这种节奏不匹配重新定义为时间表征问题，并比较三种从密集历史数据到稀疏标签的波级映射方式：水平（波内摘要）、绝对漂移（跨波变化）和比例漂移。利用Providemus阿尔茨海默病研究中82名成年人近一年的数据，我们对21项情感与认知结果进行建模。将日尺度信号简化为紧凑的波级描述符（集中趋势、离散度和分布形状），并在两个正交评估轴下用四种回归器学习：留一被试和留一波。采用跨折的均值和中位数缩放平均绝对误差（MAE）报告性能。结果显示存在差异：情感状态可通过波间绝对漂移最佳预测，而认知表现与波内水平一致，这反映了情感动态理论。在所有窗口特征中，形状描述符（如最小值、峰度）比简单均值/中位数携带更多信号。我们为稀疏标签建模贡献了一个表征三元组、一种可在设备上应用的波级特征模式，以及一种将跨被试泛化与时间鲁棒性分离的双轴报告实践。这些结果将时间表征从隐式预处理步骤转变为脑健康领域真实世界情感计算应用中明确、可测试的设计选择。
英文摘要
Longitudinal, in-the-wild, wearable sensing yields day-level physiology, sleep, activity, and environmental streams, whereas affect and cognition are labeled only episodically (per waves). We recast this cadence mismatch as a temporal representation problem and compare three wave-level mappings from dense histories to sparse labels: levels (within-wave summaries), absolute drift (change across waves), and proportional drift. Using almost a year of data from 82 adults in the Providemus alz study, we model 21 affect and cognition outcomes. Day-scale signals are reduced to compact wave-level descriptors (central tendency, dispersion, and distributional shape) and learned with four regressors under two orthogonal evaluation axes: leave-one-subject-out and leave-one-wave-out. Performance is reported as scaled MAE using both mean and median across folds. Differences emerge: affective states are best predicted by wave-to-wave absolute drift, whereas cognitive performance aligns with within-wave levels, reflecting emotion dynamic theories. Across windowing features, shape descriptors (e.g., minima, kurtosis) carry more signal than simple means/medians. We contribute a representation triad for sparse-label modelling, a wave-level feature schema applicable on-device, and a dual-axis reporting practice that separates cross-participant generalization from temporal robustness. These results convert temporal representation from an implicit preprocessing step into an explicit, testable design choice for real-world affective-computing applications in brain health.
