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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: audio |
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- name: label |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 7550569446.072 |
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num_examples: 11688 |
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download_size: 6823528468 |
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dataset_size: 7550569446.072 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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annotations_creators: |
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- expert-generated |
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license: mit |
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multilinguality: [] |
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size_categories: |
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- 10K<n<100K |
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source_datasets: [] |
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task_categories: |
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- audio-classification |
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task_ids: |
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- keyword-spotting |
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pretty_name: FluSense |
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tags: |
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- influenza |
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- audio-event |
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- flu |
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- cough |
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- sneeze |
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- classification |
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- health |
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--- |
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# FluSense |
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**FluSense** is a dataset of segmented audio events derived from the FluSense platform, a contactless influenza-like illness surveillance system. |
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This dataset is intended for use in flu symptom detection. |
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## Dataset Structure |
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Each sample includes: |
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- `audio`: audio segment (waveform and sampling rate) |
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- `label`: string label (e.g., "cough", "speech", etc.) |
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## Labels |
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The dataset includes the following sound event classes: |
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- `cough` |
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- `sneeze` |
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- `sniffle` |
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- `speech` |
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- `silence` |
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- `throat-clearing` |
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- `burp` |
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- `hiccup` |
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- `gasp` |
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- `breathe` |
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*Excluded labels include: `vomit`, `wheeze`, `snore`, and `etc`.* |
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## Source |
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Segments were extracted from original FluSense recordings and aligned using expert-generated TextGrid annotations. Each `.wav` file corresponds to a labeled interval. |
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## Use Cases |
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- Influenza symptom detection |
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- Syndromic surveillance modeling |
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- Sound event detection in healthcare environments |
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- Audio classification benchmarking |
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## License |
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This dataset is released under the **MIT License**. |
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## Citation |
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If you use this dataset, please cite the following work: |
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```bibtex |
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@article{10.1145/3381014, |
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author = {Al Hossain, Forsad and Lover, Andrew A. and Corey, George A. and Reich, Nicholas G. and Rahman, Tauhidur}, |
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title = {FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas}, |
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year = {2020}, |
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issue_date = {March 2020}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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volume = {4}, |
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number = {1}, |
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url = {https://doi.org/10.1145/3381014}, |
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doi = {10.1145/3381014}, |
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journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, |
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month = mar, |
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articleno = {Article 1}, |
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numpages = {28}, |
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keywords = {Contactless Sensing, Crowd Behavior Mining, Edge Computing, Influenza Surveillance} |
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} |
