J. Klein Brinke MSc (Jeroen)

PhD Candidate

About Me

Jeroen Klein Brinke finished his B.Sc in Technical Computer Science at the University of Twente (part of the first wave of TOM students) in 2016 with research being done in wireless sensor networks (flexible sensors in sports applications). In 2018, he received his M.Sc. (cum laude) in Technical Computer Science (specialization: Wireless and Sensor Systems) with research in deep learning and device-free sensing.

His current research still continues this trend, by actively doing research into RF-based device-free sensing in multiple domains and applying deep learning in real-life settings. He is also actively increasing his knowledge in antenna design and radio-wave/signal propagation.

He is also actively involved in his old Bachelor programme, B-TCS, as a mentor for first-year students and participating as a lecturer and supervisor in multiple BSc courses. Since 2019, he also actively helps in and contributes to the M.Sc. course Ubiquitous Computing at the University of Twente.


Engineering & Materials Science
Channel State Information
Convolutional Neural Networks
Information Analysis
Predictive Maintenance
Remote Sensing
Transfer Learning
Medicine & Life Sciences
Digital Libraries


Klein Brinke, J. , Chiumento, A. , & Havinga, P. J. M. (2021). Personal Hygiene Monitoring Under the Shower Using Wi-Fi Channel State Information. In R-H. Liang, A. Chiumento, P. Pawełczak, & M. Funk (Eds.), CHIIoT 2021: Workshops on Computer Human Interaction in IoT Applications CEUR. http://ceur-ws.org/Vol-2996/
Bagave, P., Linssen, J., Teeuw, W. , Klein Brinke, J. , & Meratnia, N. (2019). Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN). In DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis (pp. 51-56). https://doi.org/10.1145/3359427.3361917
Klein Brinke, J. , & Meratnia, N. (2019). Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning. In AIChallengeIoT ’19: International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (pp. 56-62). https://doi.org/10.1145/3363347.3363362
Klein Brinke, J. , & Meratnia, N. (2019). Dataset: Channel State Information for Different Activities, Participants and Days. In DATA’19: Proceedings of the Second Workshop on Data Acquisition To Analysis (pp. 61-64). ACM Press. https://doi.org/10.1145/3359427.3361913

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Courses Academic Year  2021/2022

Contact Details

Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 5032
Hallenweg 19
7522NH  Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  5032
P.O. Box 217
7500 AE Enschede
The Netherlands

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