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Smartphone-based Cough and
Sleep Quality Detection
1. Problem 2. Research Question
To which degree of accuracy can a mobile
application detect asthmatic nocturnal cough
and sleep quality with the smartphone’s built-in
1. Marsden et al. (2016): Nocturnal cough frequency
provides an objective assessment of asthma
symptoms that correlates with standard
measures of asthma control
2. Luyster et al. (2012): Sleep quality is associated
with asthma control even if accounted for
4. Method:Learning Pipeline
5. Expected Results
Barata, F., Kowatsch, T., Tinschert, P., Filler, A., Personal MobileCoach: TailoringBehavioral Inter ventions to the
Needs of Individual Participants, UbiComp ’16 Proceedings of the 2016 ACM International Joint Conference on
Pervasive and Ubiquitous Computing: Adjunct Workshop Designing, Developing, and Evaluating The Internet of
Personal Health (IoPH), Heidelberg, Germany, 1089-1094.
Luyster, Faith S., et al. "Sleep quality and asthma contro l and quality of life in non-severe and severe asthma." Sleep
and Breathing 16.4 (2012): 1129-1137.
Marsden, Paul A., et al. "Objective cough frequency, ai rway inflammation, and disease control in asthma." CHEST
Journal 149.6 (2016): 1460-1466.
Tinschert, P., Barata, F., Kowatsch, T., Enhancing Asthma Control through IT: Design, Impleme ntation and Planned
Evaluation of the Mobile Asthma Companion, in Leimeister, J.M.; Brenner, W. (Hrsg.): Proceedings de r 13th
International Conference on Wirtschaftsinformatik (WI 2017), St. Gallen, 1291-1294.
EPFL Lausanne | January 27-30 | 2018Applied Machine Learning Days
Raw Sensor Data
8.00 pm 10.00 am
A classification model with accuracy values close to 1 for performing
the cough detection and sleep quality estimation can be developed.
, Peter Tinschert
, Frank Rassouli
, Florent Baty
, Martin Brutsche
, Milo Puhan
, Elgar Fleisch
& Tobias Kowatsch
1 ETH Zurich, 2 University of St.Gallen, 3Cantonal Hospital St.Gallen, 4 University of Zurich & 5 medix Zurich
3. Research Framework
Example: cough detection pipeline