WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-choice questions constructed from the wearable time series, blood biomarkers, and demographics of 200 real users, each with up to 500 days of daily measurements. WearableQA preserves authentic wearable distributions that include device noise and inter-individual variability. To evaluate distinct reasoning capabilities, we introduce 16 question types organized along two complementary axes: data ve...
| Authors | Ji Soo Lee, Xilun Chen, Pierce Chuang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
Connected in the knowledge graph
- facebook/WearableQADataset · cited by
Models and datasets referencing this paper
- facebook/WearableQADataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.