发表机构
University of Bristol; University of Manchester; University of Birmingham（布里斯托大学; 曼彻斯特大学; 伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对可穿戴设备数据的结构化缺失问题，开发了保留共缺失结构的评估与训练协议，评估BRITS、SAITS等模型性能，发现模型排名依赖评估设计，为改进多传感器可穿戴数据插补策略提供了关键基础。
AI 中文摘要
可穿戴设备数据支持连续健康监测，但存在结构化缺失问题：共享同一物理传感器的特征会同时缺失。BRITS和SAITS等深度插补方法在现实缺失场景下的多模态生理数据上的评估有限，现有基准使用随机点留存协议，错误假设缺失在特征和时间上相互独立。我们利用Garmin智能手表记录的癫痫患者数据，开发了一种评估协议：从训练数据中挖掘连续缺失运行模板，按单特征缺口长度分位数分层，并将其作为保留共缺失结构的块掩码注入。一种让模型接触相同缺失分布的匹配训练协议，使BRITS在严重缺口上的平均绝对误差（MAE）降低43%，证明了所提评估和训练协议在该单参与者数据集中的潜在益处。我们进一步为BRITS添加了一天中的时间编码和昼夜节律谐波通道。没有单一模型占优：线性插值在短缺口的缓慢变化特征上表现最优；扩展后的BRITS在中度和严重缺口的动态心脏特征上MAE更低；SAITS虽MAE更高，但通过Jensen-Shannon距离能更好保留真实分布。最终，模型排名高度依赖评估设计。通过揭示传统评估方法如何掩盖模型真实能力，我们的可迁移协议为开发未来多传感器可穿戴数据集的更好插补策略奠定了关键步骤。
英文摘要
Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods such as BRITS and SAITS have seen limited evaluation on multimodal physiological data under realistic missingness, and existing benchmarks use random-point holdout protocols that incorrectly assume missingness is independent across features and time. Using data from a person with epilepsy recorded on a Garmin smartwatch, we develop an evaluation protocol that mines contiguous missing-run templates from training data, stratifies them by per-feature gap-length quantiles, and injects them as block masks with preserved co-missingness structure. A matched training protocol exposing models to the same missingness distribution reduces BRITS's severe-gap MAE by 43%, demonstrating the potential benefit of the proposed evaluation and training protocol within this single-participant dataset. We further extend BRITS with time-of-day encoding and a circadian harmonic channel. No single model dominates: linear interpolation is optimal for slow-moving features over short gaps; extended BRITS achieves lower MAE on dynamic cardiac features in moderate and severe gaps; and SAITS better preserves the ground-truth distribution by Jensen-Shannon distance despite higher MAE. Ultimately, model rankings strongly depend on evaluation designs. By exposing how traditional evaluation methods obscure true model capabilities, our transferable protocol establishes critical steps towards developing better imputation strategies for future multi-sensor wearable datasets.
