dr. J.W. Kamminga (Jacob)

Assistant Professor
EEMCS-CS-PS
Zilverling
j.w.kamminga@utwente.nl
+31534894735
Business card

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.

Expertise

  • Computer Science

    • Activity Recognition
    • Classifier
    • Accuracy
    • Annotation

  • Earth and Planetary Sciences

    • Recognition
    • Detection
    • Biodiversity

  • Physics

    • Acoustics

Organisations

Publications

2024
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification, 1-28. ArXiv.org. Hogeweg, L., Gangireddy, R., Brunink, D., Kalkman, V., Cornelissen, L. & Kamminga, J. W.https://doi.org/10.48550/arXiv.2403.06874
2022
ARISE: Building an infrastructure for species recognition and biodiversity monitoring in the Netherlands. van Ommen Kloeke, E., Huijbers, C., Beentjes, K., Kamminga, J. W., Bakker, P. A. J. & Kissling, W. D.https://doi.org/10.3897/biss.6.93613Improving the Annotation Efficiency for Animal Activity Recognition using Active LearningIn Measuring Behavior 2022: 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (pp. 51-58). Spink, S., Kamminga, J. W. & Kamilaris, A.https://doi.org/10.6084/m9.figshare.20066849
2021
Counting sea lions and elephants from aerial photography using deep learning with density maps, Article 27, 1-10. Padubidri, C., Kamilaris, A., Karatsiolis, S. & Kamminga, J.https://doi.org/10.1186/s40317-021-00247-x
2020
Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine Learning. University of Twente. Kamminga, J. W.https://doi.org/10.3990/1.9789036550550Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition. Kamminga, J. W., Meratnia, N., Le, D. V. & Havinga, P. J. M.
2019
Horsing Around: A Dataset Comprising Horse Movement, Article 131. Kamminga, J. W., Janßen, L., Meratnia, N. & Havinga, P. J. M.https://doi.org/10.3390/data4040131Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition, 22-25. Kamminga, J. W., Meratnia, N. & Havinga, P. J. M.https://doi.org/10.1145/3359427.3361908Synchronization between Sensors and Cameras in Movement Data Labeling FrameworksIn DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis (pp. 37-39). Kamminga, J. W., Jones, M., Seppi, K., Meratnia, N. & Havinga, P. J. M.https://doi.org/10.1145/3359427.3361920

Research profiles

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

Address

University of Twente

Zilverling (building no. 11)
Hallenweg 19
7522 NH Enschede
Netherlands

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