Daniel Seung Kim
Daniel Seung Kim has 2 papers in the ChatPPG research atlas (2026) and 19 co-authors in the atlas. Main topics: time-series-language-model, TSLM, sensor-language, captioning, OpenTSLM, OpenMHC.
Papers
- OpenMHC: Accelerating the Science of Wearable Foundation Models2026 · arXiv preprint
OpenMHC is a wearable health dataset from more than a decade of the My Heart Counts study app, released to qualified researchers with open-source code for recent wearable foundation models. It has more than 60 million hours of data across 19 channels, such as steps, heart rate, sleep and workouts, and up to 169 linked health and lifestyle variables from 11,894 participants.
- Published: arXiv preprint, 2026
- Cohort: 11,894 participants
- Size: 60M+ hours across 19 sensor channels
- TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data2026 · arXiv 2610.21274v1
Problem: time-series language models (TSLMs) lack open, signal-grounded, reproducible text–time-series pairs. LLM-written captions cannot be regenerated exactly. Pipeline: deterministic captioner with three extractor tiers (statistical, structural, semantic) plus a cross-channel synthesis layer.
- Venue: arXiv 2610.21274v1 (cs.LG, cs.CL; submitted 2026-10-15; 46 pp incl. supplement)
- Signals: hr, steps, sleep, activity, text
- Form factor: wrist