---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 7550569446.072
    num_examples: 11688
  download_size: 6823528468
  dataset_size: 7550569446.072
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
annotations_creators:
- expert-generated
license: mit
multilinguality: []
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- audio-classification
task_ids:
- keyword-spotting
pretty_name: FluSense
tags:
  - influenza
  - audio-event
  - flu
  - cough
  - sneeze
  - classification
  - health
---


# FluSense

**FluSense** is a dataset of segmented audio events derived from the FluSense platform, a contactless influenza-like illness surveillance system.

This dataset is intended for use in flu symptom detection.

## Dataset Structure

Each sample includes:

- `audio`: audio segment (waveform and sampling rate)
- `label`: string label (e.g., "cough", "speech", etc.)

## Labels

The dataset includes the following sound event classes:

- `cough`
- `sneeze`
- `sniffle`
- `speech`
- `silence`
- `throat-clearing`
- `burp`
- `hiccup`
- `gasp`
- `breathe`

*Excluded labels include: `vomit`, `wheeze`, `snore`, and `etc`.*

## Source

Segments were extracted from original FluSense recordings and aligned using expert-generated TextGrid annotations. Each `.wav` file corresponds to a labeled interval.

## Use Cases

- Influenza symptom detection
- Syndromic surveillance modeling
- Sound event detection in healthcare environments
- Audio classification benchmarking

## License

This dataset is released under the **MIT License**.

## Citation

If you use this dataset, please cite the following work:

```bibtex
@article{10.1145/3381014,
  author = {Al Hossain, Forsad and Lover, Andrew A. and Corey, George A. and Reich, Nicholas G. and Rahman, Tauhidur},
  title = {FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas},
  year = {2020},
  issue_date = {March 2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {4},
  number = {1},
  url = {https://doi.org/10.1145/3381014},
  doi = {10.1145/3381014},
  journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
  month = mar,
  articleno = {Article 1},
  numpages = {28},
  keywords = {Contactless Sensing, Crowd Behavior Mining, Edge Computing, Influenza Surveillance}
}