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Smart Kiosk for Screening Main Symptoms of COVID-19
We aim to develop an efficient app and algorithm to run in real-time on a kiosk prototype, collect vital signs and provide a COVID score accordingly.
Keywords: interdisciplinary approach, time series analysis, biosignal analysis, data science, medical technologies, and digital health.
The purpose of this study is to develop a prototype for an intelligent kiosk accompanied by an Infrared (IR) thermographic camera to detect contactless vital signs such as temperature measurements and provide a risk score for COVID-19.
The purpose of this study is to develop a prototype for an intelligent kiosk accompanied by an Infrared (IR) thermographic camera to detect contactless vital signs such as temperature measurements and provide a risk score for COVID-19.
- Build a prototype of a kiosk with either smartphone or a tablet
- Collect vital signs using an IR camera
- Develop a user interface
- Associate vital signs measurement with COVID-19
- Create a COVID-19 risk score (0-1, where 0 is diagnosed as a healthy subject while 1 is a subject diagnosed with COVID-19)
**Tasks**
- Literature review (10%)
- Develop a conceptual prototype for effective vital signs measurement (40%)
- Develop an app or an algorithm to differentiate between subjects with and without COVID-19 (40%)
- Report and present results (10%)
**Profile**
- Background in Medicine, Engineering, Computer Science, Biostatistics, or related fields
- Prior experience with programming (Matlab or Python)
- Able to work independently, pay attention to detail, and deliver results remotely
- Can visualize data effectively using different charts such as boxplots and scatter plots
- Background in statistics, time series analysis, and machine learning is needed.
- Build a prototype of a kiosk with either smartphone or a tablet - Collect vital signs using an IR camera - Develop a user interface - Associate vital signs measurement with COVID-19 - Create a COVID-19 risk score (0-1, where 0 is diagnosed as a healthy subject while 1 is a subject diagnosed with COVID-19)
**Tasks**
- Literature review (10%) - Develop a conceptual prototype for effective vital signs measurement (40%) - Develop an app or an algorithm to differentiate between subjects with and without COVID-19 (40%) - Report and present results (10%)
**Profile**
- Background in Medicine, Engineering, Computer Science, Biostatistics, or related fields - Prior experience with programming (Matlab or Python) - Able to work independently, pay attention to detail, and deliver results remotely - Can visualize data effectively using different charts such as boxplots and scatter plots - Background in statistics, time series analysis, and machine learning is needed.
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
Researchgate: https://www.researchgate.net/profile/Mohamed-Elgendi
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
