Register now After registration you will be able to apply for this opportunity online.
Detection of Cardiac Waves using a Smartphone
We aim to develop a machine-learning model that assesses heart activity and extract cardiac signal from a smartphone camera.
Keywords: Time series analysis, biosignal analysis, data science, medical technologies, and digital health
This study aims to develop a machine-learning algorithm or model using a smartphone camera to predict and monitor cardiac activity. The camera data shall be collected using an Android mobile application. An existing dataset will be used, and we will potentially collect our own. The algorithm could be used for clinical health studies to help monitor a user's heartbeat irregularity via a smartphone without needing additional clinical devices.
This study aims to develop a machine-learning algorithm or model using a smartphone camera to predict and monitor cardiac activity. The camera data shall be collected using an Android mobile application. An existing dataset will be used, and we will potentially collect our own. The algorithm could be used for clinical health studies to help monitor a user's heartbeat irregularity via a smartphone without needing additional clinical devices.
- Literature review (10%)
- Design and implement an optimal filter for motion artifacts (30%)
- Design and implement a multiclass machine learning classifier (40%)
- Test, compare, and evaluate results from different noise types (10%)
- Report and present results (10%)
**Your profile**
- Background in Computer Science, Biostatistics, Electrical Engineering, Biomedical Engineering 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 are highly desirable.
- Literature review (10%) - Design and implement an optimal filter for motion artifacts (30%) - Design and implement a multiclass machine learning classifier (40%) - Test, compare, and evaluate results from different noise types (10%) - Report and present results (10%)
**Your profile**
- Background in Computer Science, Biostatistics, Electrical Engineering, Biomedical Engineering 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 are highly desirable.
Dr. Moe Elgendi (moe.elgendi@hest.ethz.ch) will directly 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 directly 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
