| --- |
| 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} |
| } |
