Audio-IMU multimodal cough dataset using wearables

Cough detection is essential for long-term respiratory illness monitoring,
but clinical methods are not feasible for home use. Wearable devices offer
a convenient alternative, but challenges include data limitation and
accurately detecting coughs in real-world environments, where audio
quality may be compromised by background noise. This multimodal dataset,
collected in a controlled lab setting, includes IMU and audio data
captured using wearable devices. It was designed to support the
development of an accessible and effective cough detection system. The
dataset documentation includes details on sensor arrangement, data
collection protocol, and processing methods. Our analysis reveals that
integrating transfer learning, multimodal approaches, and
out-of-distribution (OOD) detection significantly enhances system
performance. Without OOD inputs, the model achieves accuracies of 92.59%
in the in-subject setting and 90.79% in the cross-subject setting. Even
with OOD inputs, the system maintains high accuracies of 91.97% and
90.31%, respectively, by employing OOD detection techniques, despite the
OOD inputs being double the number of in-distribution (ID) inputs. These
results are promising for developing a more efficient and user-friendly
cough and speech detection system suitable for wearable technology.