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Data Science for Digital Health
Cough events associated with COPD can be used as an indicator to assess the gravity of the sickness. The in-house developed Android app can objectively detect changes in a patient’s health from home, which would make it possible to deploy interventions before the situation becomes life-threatening.
Keywords: Machine Learning, Data Collection, Deep Learning, GANs, Health Computing, Coughs, Cough Detection, Signal Processing, Asthma, Chronic Obstructive Pulmonary Disease (COPD), Android
Since its creation, the CSS Health Lab is conducting research on digital pills. Whereas traditional pills start a chemical process in the body – generally causing the patient to feel better afterward – digital pills focus on people’s behavior: the aim is to empower patients so that they can better (self-)manage their condition on a day-to-day basis and recognize dangers to their health at an earlier stage. One of the most promising projects developed in the lab involves the detection of cough events for asthmatic and COPD patients. The goal of this project is to develop a smartphone-based passive monitoring system to enable patients and their physicians to follow the development of their condition and to raise an alert before the sickness becomes life-threatening. This monitoring has the potential to change the patient’s relationship with their sickness and to enable them to live their lives with (almost) any constrains.
This project is a unique opportunity to see your work implemented, tested, and used in the field to collect data and to take a pioneering role in the field of digital health. The research results will be published in scientific outlets.
Since its creation, the CSS Health Lab is conducting research on digital pills. Whereas traditional pills start a chemical process in the body – generally causing the patient to feel better afterward – digital pills focus on people’s behavior: the aim is to empower patients so that they can better (self-)manage their condition on a day-to-day basis and recognize dangers to their health at an earlier stage. One of the most promising projects developed in the lab involves the detection of cough events for asthmatic and COPD patients. The goal of this project is to develop a smartphone-based passive monitoring system to enable patients and their physicians to follow the development of their condition and to raise an alert before the sickness becomes life-threatening. This monitoring has the potential to change the patient’s relationship with their sickness and to enable them to live their lives with (almost) any constrains.
This project is a unique opportunity to see your work implemented, tested, and used in the field to collect data and to take a pioneering role in the field of digital health. The research results will be published in scientific outlets.
The aim of this project is for the student(s) to take a leading role in the enhancement and evaluation of the in-house developed cough detection mobile app by re-training the Deep Learning models that have recently been published by the group. To make those models more performant, the candidate(s) might need to scrap audio files from the web and develop state-of-the-art data augmentation techniques involving GANs. Furthermore, the selected student(s) will also gain some hands-on experience in optimizing and testing the Android mobile app, for its use during a clinical pilot study happening between September and October 2020 in St. Gallen.
**Related Work**
- Barata, Filipe, et al. "Towards device-agnostic mobile cough detection with convolutional neural networks." 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2019.
- Tinschert, Peter, et al. "The potential of mobile apps for improving asthma self-management: a review of publicly available and well-adopted asthma apps." JMIR mHealth and uHealth 5.8 (2017): e113.
The aim of this project is for the student(s) to take a leading role in the enhancement and evaluation of the in-house developed cough detection mobile app by re-training the Deep Learning models that have recently been published by the group. To make those models more performant, the candidate(s) might need to scrap audio files from the web and develop state-of-the-art data augmentation techniques involving GANs. Furthermore, the selected student(s) will also gain some hands-on experience in optimizing and testing the Android mobile app, for its use during a clinical pilot study happening between September and October 2020 in St. Gallen.
**Related Work**
- Barata, Filipe, et al. "Towards device-agnostic mobile cough detection with convolutional neural networks." 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2019. - Tinschert, Peter, et al. "The potential of mobile apps for improving asthma self-management: a review of publicly available and well-adopted asthma apps." JMIR mHealth and uHealth 5.8 (2017): e113.
David Cleres (dcleres@ethz.ch), Ph.D. Candidate
Center for Digital Health Interventions (Prof. Dr. Elgar Fleisch)
If you are interested, just send us an email with your CV, transcript, and a brief overview of your motivations and we’ll get in contact for all the details and find a good setup.
David Cleres (dcleres@ethz.ch), Ph.D. Candidate
Center for Digital Health Interventions (Prof. Dr. Elgar Fleisch)
If you are interested, just send us an email with your CV, transcript, and a brief overview of your motivations and we’ll get in contact for all the details and find a good setup.