Simon Perneel
Data scientist
Imec-mict-UGent'
I am a researcher at Imec-mict-UGent, where I study the relationship between smartphone use and wellbeing. My work focuses on analyzing smartphone trace data to uncover usage patterns, detect trends in smartphone use over the years, and ultimately predict moods and stress levels from these patterns. This data is collected with our research tool, the MobileDNA app.
Education
KU Leuven
Master Artificial Intelligence
Thesis: Identification of features for the optimal recognition of physical activities with wearable devices
KU Leuven, Campus Ghent
Master Industrial Engineering: Electronics-ICT
Thesis: Examination of Low-Power Wide-Area networks for IoT applications
Experience
Data engineer/scientist — Imec-mict-UGent
Looking for patterns in people's smartphone behavior and use of apps. Investigating whether smartphone use can be a predictor for ones (digital) wellbeing (stress, headaches, moods, online vigilance). Responsible for the MobileDNA research tool. MobileDNA is an app developed by imec-mict-UGent to monitor people's smartphone usage.
Portfolio
Optimizing human activity recognition with inertial sensors
For my master's thesis, I developed a human activity recognition system using wearable Magnetic Inertial Measurement Units (MIMUs). I compared feature-based and 'raw' inertial data-based classification approaches, evaluating sensor placement, feature selection, and the value of magnetometer data. The final model, using only accelerometer and gyroscope features from 3 optimally placed sensors, achieved an accuracy of 97% and F1-score of 98% on 7 common physical activities.