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Optimal Heart Rate Detection using a Smartphone
We aim to develop a machine learning model for accelerometer and camera data collected from a user’s smartphone that can identify the user’s heart rate.
Keywords: Time series analysis, biosignal analysis, data science, medical technologies, and digital health
This study aims to develop a machine learning algorithm or model that can use accelerometer data from a smartphone to predict and monitor heart rate. The accelerometer and camera data shall be collected using an Android mobile application for digital phenotyping. These algorithms could be used for clinical studies to help monitor a user’s behavior and circadian rhythm via a smartphone without additional wearable sensors.
This study aims to develop a machine learning algorithm or model that can use accelerometer data from a smartphone to predict and monitor heart rate. The accelerometer and camera data shall be collected using an Android mobile application for digital phenotyping. These algorithms could be used for clinical studies to help monitor a user’s behavior and circadian rhythm via a smartphone without additional wearable sensors.
- Literature review (10%)
- Collect accelerometer and camera smartphone app (10%)
- Design and implement an optimal filter for motion artifacts (30%)
- Design and implement a multiclass machine - learning classifier (30%)
- Test, compare, and evaluate results from different noise types (10%)
- Report and present results (10%)
**Your 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.
- Literature review (10%) - Collect accelerometer and camera smartphone app (10%) - Design and implement an optimal filter for motion artifacts (30%) - Design and implement a multiclass machine - learning classifier (30%) - Test, compare, and evaluate results from different noise types (10%) - Report and present results (10%)
**Your 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
