  We present an automatic non-invasive way of detecting cough events based on
both accelerometer and audio signals.
  The acceleration signals are captured by a smartphone firmly attached to the
patient's bed, using its integrated accelerometer.
  The audio signals are captured simultaneously by the same smartphone using an
external microphone.
  We have compiled a manually-annotated dataset containing such
simultaneously-captured acceleration and audio signals for approximately 6000
cough and 68000 non-cough events from 14 adult male patients in a tuberculosis
clinic.
  LR, SVM and MLP are evaluated as baseline classifiers and compared with deep
architectures such as CNN, LSTM, and Resnet50 using a leave-one-out
cross-validation scheme.
  We find that the studied classifiers can use either acceleration or audio
signals to distinguish between coughing and other activities including
sneezing, throat-clearing, and movement on the bed with high accuracy.
  However, in all cases, the deep neural networks outperform the shallow
classifiers by a clear margin and the Resnet50 offers the best performance by
achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals
respectively.
  While audio-based classification consistently offers a better performance
than acceleration-based classification, we observe that the difference is very
small for the best systems.
  Since the acceleration signal requires less processing power, and since the
need to record audio is sidestepped and thus privacy is inherently secured, and
since the recording device is attached to the bed and not worn, an
accelerometer-based highly accurate non-invasive cough detector may represent a
more convenient and readily accepted method in long-term cough monitoring.
