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The paper's core contribution is a deterministic, LLM-free pipeline for generating text-annotated wearable time-series data, which addresses a real bottleneck for training time-series language models. Its headline result, that caption-augmented training beat a label-only baseline on 29 of 32 per-user evaluations, is promising but limited to one dataset and one model family. Readers should watch whether the reproducibility claims hold up in independent reuse, and whether the health-outcome gains generalize beyond MyHeart Counts.
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
arXiv:2610.21274v1 Announce Type: cross 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.
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.
46 pages, 27 figures, including supplementary material. Code: this https URL
Machine Learning (cs.LG); Computation and Language (cs.CL)
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)
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