TimeCap：面向可穿戴健康时间序列数据的可扩展开源标注流水线
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
研究团队发布开源确定性标注流水线 TimeCap，无需语言模型参与、直接基于信号生成标注，可通过编写通道定义扩展至新 schema。将其应用于 MyHeart Counts 可穿戴数据集 OpenMHC 后生成 MHCap，覆盖 19 个 Apple HealthKit 通道、908,944 人日共 7,005,763 条标注。
Authors:Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck, Narayan Schuetz, Thomas Kaar, Manuel Milling, Nicolas Zumarraga, Ning Wang, Kelly W. Zhang, Anders Johnson, Robert Jakob, Björn W. Schuller, Elgar Fleisch, Daniel Seung Kim, Allan Lawrie, Patrick Langer, Maxwell A. Xu, Euan Ashley, Paul Schmiedmayer
Abstract:Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processing and interpretation particularly challenging. Time-Series Language Models (TSLMs) promise scalable interpretation of these signals, yet remain bottlenecked by a fundamental lack of text-annotated time series pairs that are signal-grounded, reproducible, and extensible across modalities and schemas. Existing paired corpora are either closed or limited in scale and modality coverage. Annotation with large language models could scale them up, but its outputs cannot be regenerated exactly, which prevents others from re-deriving, extending, or auditing the resulting corpus. To address this, we release TimeCap, an open deterministic captioning pipeline with three reusable extractor tiers and a cross-channel synthesis layer that operates directly on the signal with no language model in the loop, extensible to new schemas by writing channel definitions alone. This positions TimeCap as a reproducible mechanism for future dataset generation rather than a one-off release. Applying TimeCap to OpenMHC, the MyHeart Counts wearable dataset, yields MHCap, the largest open caption layer for multimodal wearable time-series data. MHCap spans 19 Apple HealthKit channels and 7,005,763 captions over 908,944 person-days. To showcase the impact of the corpus, we train OpenTSLM, a time-series language model, on MHCap and benchmark on health-outcome prediction. Caption-augmented training (OpenTSLM-Cap) outperforms a label-only baseline on 29 of 32 per-user binary evaluations across 8 tasks.
| Comments: | 46 pages, 27 figures, including supplementary material. Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.21274 [cs.LG] |
| (or arXiv:2610.21274v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21274 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Paul Schmiedmayer [view email]
[v1]
Thu, 15 Oct 2026 05:10:01 UTC (3,677 KB)
来源：arXiv cs.CL · arxiv.org