Jonas Van Der Donckt
Orcid: 0000-0003-1440-7410
According to our database1,
Jonas Van Der Donckt
authored at least 16 papers
between 2020 and 2025.
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Bibliography
2025
Uncovering the potential of smartphones for behavior monitoring during migraine follow-up.
BMC Medical Informatics Decis. Mak., December, 2025
SoftwareX, 2025
2024
Addressing Data Quality Challenges in Observational Ambulatory Studies: Analysis, Methodologies and Practical Solutions for Wrist-worn Wearable Monitoring.
CoRR, 2024
Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities.
Proceedings of the Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2024
Left-Right Swapping and Upper-Lower Limb Pairing for Robust Multi-Wearable Workout Activity Detection.
Proceedings of the Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2024
2023
Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring.
Biomed. Signal Process. Control., March, 2023
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines.
CoRR, 2023
Proceedings of the 2023 IEEE Visualization and Visual Analytics (VIS), 2023
2022
BMC Medical Informatics Decis. Mak., 2022
mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients.
BMC Medical Informatics Decis. Mak., 2022
Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring.
CoRR, 2022
Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders.
Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022
Proceedings of the 2022 IEEE Visualization and Visual Analytics (VIS), 2022
2021
MIRRA: A Modular and Cost-Effective Microclimate Monitoring System for Real-Time Remote Applications.
Sensors, 2021
2020
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals.
Proceedings of the SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Seoul, Republic of Korea, 29 June, 2020