About Me
Jacob Kamminga completed his Ph.D. in animal activity recognition at the University of Twente in 2020. He used machine learning models as activity classifiers to recognize various activities of animals using motion data on resource-constrained devices (edge-AI). Currently, he is studying active learning and human-in-the-loop AI training. Furthermore, he is interested in unsupervised representation learning to exploit unlabeled data. Other areas of his expertise are data acquisition, processing, and annotation.
Since 2021 Jacob has been the digital species identification team leader within the ARISE biodiversity project. The digital species identification team builds services that support developing and deploying AI algorithms that detect and identify species from various digital media such as sound, images, and radar.
Since 2021 Jacob has been the digital species identification team leader within the ARISE biodiversity project. The digital species identification team builds services that support developing and deploying AI algorithms that detect and identify species from various digital media such as sound, images, and radar.
Expertise
Engineering & Materials Science
# Accelerometers
# Acoustic Waves
# Aerial Photography
# Animals
# Classifiers
# Sensors
# Smartphones
Physics & Astronomy
# Aerial Photography
Organisations
Publications
Recent
Hogeweg, L., Gangireddy, R., Brunink, D., Kalkman, V., Cornelissen, L.
, & Kamminga, J. W. (2024).
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification. (pp. 1-28). ArXiv.org.
https://doi.org/10.48550/arXiv.2403.06874
van Ommen Kloeke, E., Huijbers, C., Beentjes, K.
, Kamminga, J. W., Bakker, P. A. J., & Kissling, W. D. (2022).
ARISE: Building an infrastructure for species recognition and biodiversity monitoring in the Netherlands. Abstract from Biodiversity Information Standards, TDWG 2022, Sofia, Bulgaria.
https://doi.org/10.3897/biss.6.93613
Spink, S.
, Kamminga, J. W.
, & Kamilaris, A. (2022).
Improving the Annotation Efficiency for Animal Activity Recognition using Active Learning. In A. Spink, J. Barski, A.-M. Brouwer, G. Riedel, & A. Sil (Eds.),
Measuring Behavior 2022: 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (Vol. 2, pp. 51-58)
https://doi.org/10.6084/m9.figshare.20066849
Padubidri, C.
, Kamilaris, A., Karatsiolis, S.
, & Kamminga, J. (2021).
Counting sea lions and elephants from aerial photography using deep learning with density maps.
Animal Biotelemetry,
9(1), 1-10. Article 27.
https://doi.org/10.1186/s40317-021-00247-x
Kamminga, J. W. (2020).
Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine Learning. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente.
https://doi.org/10.3990/1.9789036550550
Kamminga, J. W.
, Meratnia, N.
, Le, D. V.
, & Havinga, P. J. M. (2020).
Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition. Paper presented at 6th Workshop on Mining and Learning from Time Series, MiLeTS 2020.
Kamminga, J. W.
, Meratnia, N.
, & Havinga, P. J. M. (2019).
Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition. 22-25. Paper presented at 2nd Workshop on Data Acquisition To Analysis, DATA 2019, New York, New York, United States.
https://doi.org/10.1145/3359427.3361908
Kamminga, J. W., Jones, M., Seppi, K.
, Meratnia, N.
, & Havinga, P. J. M. (2019).
Synchronization between Sensors and Cameras in Movement Data Labeling Frameworks. In
DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis (pp. 37-39)
https://doi.org/10.1145/3359427.3361920
Kamminga, J. W., Janßen, L.
, Meratnia, N.
, & Havinga, P. J. M. (2019).
Horsing Around: A Dataset Comprising Horse Movement.
Data,
4(4), Article 131.
https://doi.org/10.3390/data4040131
UT Research Information System
Google Scholar Link
Courses Academic Year 2023/2024
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses Academic Year 2022/2023
Contact Details
Visiting Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(building no. 11)
Hallenweg 19
7522NH Enschede
The Netherlands
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(building no. 11)
Hallenweg 19
7522NH Enschede
The Netherlands
Mailing Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
P.O. Box 217
7500 AE Enschede
The Netherlands