MSc thesis project proposal

[2022] Smart Vital Signs Monitoring in Wearables [in collaboration with NXP]

Project outside the university

NXP

Wearables for vital sign monitoring are becoming ever more pervasive in our day-to-day and bring the promise of Nyquist rate health monitoring. However, for them to become fully autonomous, there is a need to integrate signal processing and machine learning algorithms on-chip capable of extracting useful information from the data. This brings a challenge in terms of the power and area constraints in wearable technology and requires novel algorithm formulations.

Assignment

This project will be carried out in collaboration with NXP in Eindhoven. You will explore signal processing and machine-learning algorithms to analyze data from various NXP sensors for vitals monitoring (e.g., heartbeat, respiration rate, oxygen saturation, skin moisture, etc.). You will implement these algorithms using the NXP eIQ development tool and their optimized microcontrollers for DSP and ML within the constraints of a wearable device. Finally, you will validate the performance through experimental testing and data collection.

Requirements

MSc EE/BME student.

You should be comfortable with signal processing, machine learning, and embedded systems programming. Curiosity, hard work, and creativity are always needed. If interested, contact Dr. Dante Muratore and Dr. Francesco Fioranelli via email with a motivation letter and attached CV (with taken courses and grades). 

Contact

dr. Dante Muratore

Bioelectronics Group

Department of Microelectronics

Last modified: 2022-06-23