# TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers

arXiv: https://arxiv.org/abs/2003.08474 · HTML: https://mlsanity.com/paper/2003.08474/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 13 of 13)

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- [More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors](https://mlsanity.com/paper/2202.08267/) (2022)
- [Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings](https://mlsanity.com/paper/2305.00111/) (2023)
- [Pattern Discovery in Time Series with Byte Pair Encoding](https://mlsanity.com/paper/2106.00614/) (2021)
- [Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings](https://mlsanity.com/paper/2401.05367/) (2023)
- [Learning Behavioral Representations of Routines From Large-scale Unlabeled Wearable Time-series Data Streams using Hawkes Point Process](https://mlsanity.com/paper/2307.04445/) (2023)
- [Learning Longitudinal Stress Dynamics from Irregular Self-Reports via Time Embeddings](https://mlsanity.com/paper/2509.01569/) (2025)
- [WatchAnxiety: A Transfer Learning Approach for State Anxiety Prediction from Smartwatch Data](https://mlsanity.com/paper/2509.13725/) (2025)
- [Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults](https://mlsanity.com/paper/2606.00345/) (2026)

## Abstract (arXiv)

We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.