Datasets:
outward_facing_mic audioduration (s) 2.64 17.6 | body_facing_mic audioduration (s) 2.64 17.6 | imu array 2D | ground_truth dict | subject_id stringclasses 10
values | gender stringclasses 2
values | bmi float32 18.3 36.4 | trial int8 1 3 | movement stringclasses 2
values | noise stringclasses 4
values | sound stringclasses 4
values | duration_seconds float32 2.64 17.6 | num_coughs int16 0 25 | has_ground_truth bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
[
0.46000000834465027,
1.409999966621399,
127.44000244140625,
-8.390000343322754,
0.11999999731779099,
-6.369999885559082
],
[
0.44999998807907104,
1.409999966621399,
127.43000030517578,
-8.430000305175781,
0.11999999731779099,
-6.329999923706055
],
[
0.43... | {
"start_times": [
0.5070624947547913,
0.9168750047683716,
1.3494999408721924,
1.7300000190734863,
2.7289373874664307,
3.0394999980926514,
3.2899999618530273,
4.402187347412109,
4.7151875495910645,
5.435625076293945,
5.659999847412109,
6.23981237411499,
6.9592499732... | 76918 | Female | 36.43 | 1 | sit | music | cough | 7.749625 | 13 | true | ||
[[-0.5799999833106995,0.0,122.94000244140625,-8.75,-0.1899999976158142,-5.699999809265137],[-0.56999(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | music | laugh | 12.872625 | 0 | false | ||
[[0.09000000357627869,0.10999999940395355,124.18000030517578,-8.510000228881836,-0.02999999932944774(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | music | deep_breathing | 10.702375 | 0 | false | ||
[[0.10000000149011612,2.880000114440918,118.12000274658203,-9.020000457763672,0.5199999809265137,-4.(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | music | throat_clearing | 11.638187 | 0 | false | ||
[[-0.38999998569488525,1.600000023841858,144.5500030517578,-5.96999979019165,0.3199999928474426,-8.5(...TRUNCATED) | {"start_times":[0.6801249980926514,1.1676875352859497,1.6964374780654907,2.2536873817443848,2.867312(...TRUNCATED) | 76918 | Female | 36.43 | 1 | sit | nothing | cough | 8.437625 | 12 | true | ||
[[0.7900000214576721,2.759999990463257,134.32000732421875,-6.670000076293945,0.1599999964237213,-8.0(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | nothing | laugh | 9.027375 | 0 | false | ||
[[-0.14000000059604645,2.140000104904175,141.16000366210938,-6.599999904632568,0.4399999976158142,-8(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | nothing | deep_breathing | 10.129687 | 0 | false | ||
[[-0.9300000071525574,0.9900000095367432,139.13999938964844,-6.829999923706055,0.20000000298023224,-(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | nothing | throat_clearing | 11.77275 | 0 | false | ||
[[-0.3700000047683716,-0.11999999731779099,130.8300018310547,-7.769999980926514,0.05000000074505806,(...TRUNCATED) | {"start_times":[1.1526249647140503,1.510812520980835,1.940250039100647,2.3396875858306885,3.18575000(...TRUNCATED) | 76918 | Female | 36.43 | 1 | sit | someone_else_cough | cough | 7.312938 | 10 | true | ||
[[0.36000001430511475,2.759999990463257,125.58000183105469,-8.359999656677246,0.4399999976158142,-6.(...TRUNCATED) | {
"start_times": [],
"end_times": []
} | 76918 | Female | 36.43 | 1 | sit | someone_else_cough | laugh | 8.796 | 0 | false |
Edge Artificial Intelligence (edge-AI) Cough Counting
This is a mirrored dataset of edge-AI Cough Counting Dataset, converted to Parquet format and hosted on Hugging Face for more accessibility.
Additional preprocessing has been made, such as data aggregation and audio visualizations, for in-depth data details without downloading the whole dataset.
Background
Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an Edge-AI fashion. To advance this research field, our team from the Embedded Systems Laboratory (ESL) of EPFL contributed the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, a variety of non-cough sounds and motion scenarios mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development.
Data access
The edge-AI cough counting dataset can be found at the following Zenodo link: https://zenodo.org/record/7562332#.Y87MenbMKUm
Citations
If you use the open-source dataset in your work, please cite our publication:
@inproceedings{orlandic_multimodal_2023,
address = {Sydney, Australia},
title = {A {Multimodal} {Dataset} for {Automatic} {Edge}-{AI} {Cough} {Detection}},
copyright = {https://doi.org/10.15223/policy-029},
url = {https://ieeexplore.ieee.org/document/10340413/},
doi = {10.1109/EMBC40787.2023.10340413},
language = {en},
urldate = {2024-04-10},
booktitle = {2023 45th {Annual} {International} {Conference} of the {IEEE} {Engineering} in {Medicine} \& {Biology} {Society} ({EMBC})},
publisher = {IEEE},
author = {Orlandic, Lara and Thevenot, Jérôme and Teijeiro, Tomas and Atienza, David},
month = jul,
year = {2023},
pages = {1--7},
}
Contact
For questions or suggestions, please contact lara.orlandic@epfl.ch.
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