Datasets:
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---
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}
} |