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Smartphone Camera for Blood Pressure Classification
Our goal is to develop a smartphone camera-based model capable of distinguishing between hypotensive, normotensive, and hypertensive subjects.
Keywords: Data analytics, 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.
The research will be performed at ETH Zurich's Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. If you are interested and have questions regarding this project, please get in touch with Dr. Moe Elgendi (moe.elgendi@hest.ethz.ch).
For research-related topics, Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
The research will be performed at ETH Zurich's Biomedical and Mobile Health Technology research group (www.bmht.ethz.ch) in the Balgrist Campus in Zurich, Switzerland. If you are interested and have questions regarding this project, please get in touch with Dr. Moe Elgendi (moe.elgendi@hest.ethz.ch).
For research-related topics, Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
