Hi! I am Thorir, and I am currently a Ph.D. student at ETH Zurich under the supervision of Prof. Dr. Luca Benini. My research interest is applying Robust and Practical Machine Learning approaches, focusing on a bio-signal analysis taking into account the characteristics and constraints of wearable edge devices and IoT units. A use case that I am currently researching is the usage of bio-signals to detect and forecast seizures, where the bio-signals come from wearable devices and are classified with ML and DL algorithms on the same wearable edge devices.
Ph.D. in Electrical Engineering and Information Technology
ETH Zurich
M.Sc. in Electrical Engineering and Information Technology, 2020
ETH Zurich
B.Sc. in Electrical and Computer Engineering, 2018
University of Iceland
The paper presents a combined seizure and artifact detection framework based on Gradient Boosted Trees. The framework achieves high accuracy in detecting seizures and artifacts, reducing false alarms. The algorithms are optimized for a Parallel Ultra-Low Power platform, enabling extended monitoring with a long battery lifespan. The paper highlights the benefits of integrating artifact detection in wearable epilepsy monitoring devices.
In this paper, we present implementations of energy-efficient artifact detection algorithms on a parallel ultra-low power platform.
In this paper, we present Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS search spaces.
In this paper, we present implementations of seizure detection algorithms on a parallel ultra-low power platform.