  We have performed cough detection based on measurements from an accelerometer
attached to the patient's bed. This form of monitoring is less intrusive than
body-attached accelerometer sensors, and sidesteps privacy concerns encountered
when using audio for cough detection. For our experiments, we have compiled a
manually-annotated dataset containing the acceleration signals of approximately
6000 cough and 68000 non-cough events from 14 adult male patients in a
tuberculosis clinic. As classifiers, we have considered convolutional neural
networks (CNN), long-short-term-memory (LSTM) networks, and a residual neural
network (Resnet50). We find that all classifiers are able to distinguish
between the acceleration signals due to coughing and those due to other
activities including sneezing, throat-clearing and movement in the bed with
high accuracy. The Resnet50 performs the best, achieving an area under the ROC
curve (AUC) exceeding 0.98 in cross-validation experiments. We conclude that
high-accuracy cough monitoring based only on measurements from the
accelerometer in a consumer smartphone is possible. Since the need to gather
audio is avoided and therefore privacy is inherently protected, and since the
accelerometer is attached to the bed and not worn, this form of monitoring may
represent a more convenient and readily accepted method of long-term patient
cough monitoring.
