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Blood Pressure Assessment Using a Selfie Video
We aim to develop a smartphone app to differentiate between hypotensive, normotensive, and hypertensive subjects.
Keywords: Android App development, image processing analysis, biosignal analysis, data science, medical technologies, and digital health
This study aims to develop a smartphone app combined with a machine-learning algorithm to assess blood pressure levels using a selfie video. The algorithm needs to identify face areas that are associated with changes in blood pressure measurements. Also, common noise types that could impact the quality of the selfie video, such as hand movement, will be investigated.
This study aims to develop a smartphone app combined with a machine-learning algorithm to assess blood pressure levels using a selfie video. The algorithm needs to identify face areas that are associated with changes in blood pressure measurements. Also, common noise types that could impact the quality of the selfie video, such as hand movement, will be investigated.
- App development
- Face detection (using open-access software)
- Feature extraction
- Model development
**Profile**
- Background in 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.
- App development - Face detection (using open-access software) - Feature extraction - Model development
**Profile**
- Background in 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