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Data Science for Driver Health
With a unique dataset of driving data we want to answer questions related to physiological driver health. We thus want to design a comprehensive data analytics pipeline based on one or more of the data streams. This includes preprocessing and analysis as well as building machine learning models.
Keywords: machine learning, car driving, data analysis, health computing, can data
At the **Bosch Internet of Things Lab** (a cooperation of ETH Zürich, University of St. Gallen and Bosch), we think, **intelligent cars and connected mobility** will have a huge impact on our lives in the near future.
**Want to be part of it?**
We have a unique dataset of driving data which we want to analyze to answer questions related to physiological driver health.
For this purpose, we will take into account the following data streams:
- CAN (car data)
- Environment data
- Video (facial expressions & street)
- Eye-tracking information
- Physiological data (ECG, PPG, movements and other health-related sensor readings)
Within this area, we have many possibilities for you to design a **comprehensive data analytics pipeline** based on one or more of the available data streams. This includes preprocessing and analysis as well as building machine learning models.
We offer a **well-structured Master thesis supervision** that should help keeping you on track where you need it and leave room for your own creativity where you want it.
The thesis will be carried out in close collaboration with the Driver Monitoring group at Robert Bosch GmbH. See https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-cars-and-light-commercial-vehicles/interior-and-body-systems/interior-monitoring-systems/
See the attached document for further details.
At the **Bosch Internet of Things Lab** (a cooperation of ETH Zürich, University of St. Gallen and Bosch), we think, **intelligent cars and connected mobility** will have a huge impact on our lives in the near future.
**Want to be part of it?**
We have a unique dataset of driving data which we want to analyze to answer questions related to physiological driver health. For this purpose, we will take into account the following data streams:
- CAN (car data)
- Environment data
- Video (facial expressions & street)
- Eye-tracking information
- Physiological data (ECG, PPG, movements and other health-related sensor readings)
Within this area, we have many possibilities for you to design a **comprehensive data analytics pipeline** based on one or more of the available data streams. This includes preprocessing and analysis as well as building machine learning models.
We offer a **well-structured Master thesis supervision** that should help keeping you on track where you need it and leave room for your own creativity where you want it.
The thesis will be carried out in close collaboration with the Driver Monitoring group at Robert Bosch GmbH. See https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-cars-and-light-commercial-vehicles/interior-and-body-systems/interior-monitoring-systems/
See the attached document for further details.
Not specified
Martin Maritsch (mmaritsch@ethz.ch)
If you are interested, just send us an email with your **CV**, **transcript**, and **motivational statement** and we’ll get in contact for all the details and find a good setup.
Martin Maritsch (mmaritsch@ethz.ch)
If you are interested, just send us an email with your **CV**, **transcript**, and **motivational statement** and we’ll get in contact for all the details and find a good setup.