Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Digital Healthcare Applications on Single-Board Computers
The goal of this project is to develop a single-board computer based passive monitoring system to enable patients and their physicians to follow the development of their chronic condition and to raise an alert before the sickness become life-threatening.
Keywords: Embedded Systems, Raspberry Pi, Health Computing, Cough Detection, Signal Processing, Chronic Obstructive Pulmonary Disease (COPD), Android, Tensorflow, LineageOS
To manage the increasing number of patients with with chronic diseases and reduce the social and economic burden of treatment, healthcare providers have sought to implement so called remote patient monitoring (RPM). These RPM application come with a lot of avantages as they are a scalable, show beneficial health outcomes for patient, and are low-cost. Nevertheless, these technologies do not come without challenges to the elderly population that they actually target. For this reason, our groups has recently started to develop voice-based conversational agents that can interact in a more natural way with patients. In our most recent publication, we developed, Lena, a voice-assitant running on a Raspberry Pi 4B able to communicate with the patient. As this pilot study showed very promising results, our next goal is to transform this system into a more sofisticated health monitoring hub and you could become a driving force in this project!
To manage the increasing number of patients with with chronic diseases and reduce the social and economic burden of treatment, healthcare providers have sought to implement so called remote patient monitoring (RPM). These RPM application come with a lot of avantages as they are a scalable, show beneficial health outcomes for patient, and are low-cost. Nevertheless, these technologies do not come without challenges to the elderly population that they actually target. For this reason, our groups has recently started to develop voice-based conversational agents that can interact in a more natural way with patients. In our most recent publication, we developed, Lena, a voice-assitant running on a Raspberry Pi 4B able to communicate with the patient. As this pilot study showed very promising results, our next goal is to transform this system into a more sofisticated health monitoring hub and you could become a driving force in this project!
This project aims for the student(s) to take a leading role in the enhancement in-house developed single-board computer based voice assistant that has recently been published by the group (D.Cleres et al. 2021). To make the current system more performant, more robust, and easier to use, the candidate(s) might need to revise the code architecture of the system, add additional sensors, and develop a better understanding of the needs in terms of hardware and software to transform the current prototype into a functional system that can be used by patients. Furthermore, the selected student(s) will also gain hands-on experience with handling the integration of deep learning models as well as on how to integrate commercially available sensors on single-board computers such as Raspberry Pis.
In our opinion this project is a unique opportunity to discover the healthcare domain from the angle of a software and hardware provider and to conduct a study on actual patients in their daily environment and to discover all the challenges linked to provide digital healthcare solutions for patients with chronic diseases.
This project aims for the student(s) to take a leading role in the enhancement in-house developed single-board computer based voice assistant that has recently been published by the group (D.Cleres et al. 2021). To make the current system more performant, more robust, and easier to use, the candidate(s) might need to revise the code architecture of the system, add additional sensors, and develop a better understanding of the needs in terms of hardware and software to transform the current prototype into a functional system that can be used by patients. Furthermore, the selected student(s) will also gain hands-on experience with handling the integration of deep learning models as well as on how to integrate commercially available sensors on single-board computers such as Raspberry Pis. In our opinion this project is a unique opportunity to discover the healthcare domain from the angle of a software and hardware provider and to conduct a study on actual patients in their daily environment and to discover all the challenges linked to provide digital healthcare solutions for patients with chronic diseases.
ETH Zürich
David Cleres
WEV G 217
Weinbergstrasse 56/58
8092 Zürich
dcleres@ethz.ch
https://www.c4dhi.org/projects/digital-biomarker-copd/
ETH Zürich David Cleres WEV G 217 Weinbergstrasse 56/58 8092 Zürich