research
- Mental Health Topics
- OCD (obsessive-compulsive disorder)
- Bipolar (manic-depressive)
- Biofeedback
- Human Activity Recognition
- Indoor Positioning Systems
- Wearable Sensors
- Multimodal Data Collection
teaching
Projects Supervision
| Winter Term 2022/23 | Bachelor Project | Does my watch know that I need a break? Integration of a biofeedback module into the SensorHub system |
| Summer Term 2020 | Master Project | What Can Your Smartwatch Tell You About Your Mental Health? OCDC – An Obsessive Compulsive Disorder Classification System |
| Winter Term 2019/20 | Bachelor Project | Unobtrusive Health Monitoring for Driving Lifestyle Changes using Wearables |
Supervised Theses
Bachelor
| 2021 | Julia Joch | Gesture recognition in daily life as a means of noninvasive labeling in medical studies |
| 2020 | Kira Grammel | Sleep Stage Segmentation using IMU and PPG Sensors |
| 2020 | Kira Weinlein | Emotion Recognition using Wearables |
Master
| 2021 | Fabian Stolp | Active Learning in Personalized OCD Recognition |
| 2021 | Lando Löper | Personalised Sensor-Based OCD Detection Using Federated Learning |
| 2020 | Martin Schlegel | Activity Recognition in the Context of Obsessive-Compulsive Disorder Detection |
publications
2025
Kirsten, K., Burchard, R., Mackintosh, A., Miché, M., Bentz, D., Bader, K., Behr, J., Lieb, R., Van Laerhoven, K., Arnrich, B., & Wahl, K. (2025, October 12–16). Exploring wearable-based detection of compulsive handwashing in a non-controlled setting: A case study. Proceedings of the 2025 ACM International Symposium on Wearable Computers (ISWC ’25). Association for Computing Machinery. https://doi.org/10.1145/3715071.3750434
Kirsten, K., Burchard, R., Bauer, O., Miché, M., Scholl, P., Wahl, K., Lieb, R., Van Laerhoven, K., & Arnrich, B. (2025). The supervised learning dilemma: Lessons learned from a study in the wild. In Sensor-Based Activity Recognition and Artificial Intelligence (pp. 181–195). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80856-2_13
Albert, J., Zhou, L., Kirsten, K., Kaynak, N., Rackoll, T., Walz, T., Weese, D., Kos, R., Nave, A. H., & Arnrich, B. (2025). Using wearable sensors in stroke rehabilitation. In Sensor-Based Activity Recognition and Artificial Intelligence (pp. 277–282). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80856-2_19
Kirsten, K., Walz, T., Weese, D., & Arnrich, B. (2025). Toward unobtrusive monitoring of everyday activities using multimodal wearable and ambient data: A multiroom living lab feasibility study. In Sensor-Based Activity Recognition and Artificial Intelligence. Springer Nature Switzerland. (10th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence [iWOAR 2025], September 18–19, 2025, Enschede, Netherlands).
2024
Kirsten, K., Albert, J., Zhou, L., Kos, R., Al-Saeedi, S., Walz, T., Weese, D., & Arnrich, B. (2024, January). Concept for an unobtrusive system to detect compulsive behavior at home using wearables and indoor positioning. Invited talk at the 1st DGDM Symposium for Digital Medicine, Potsdam, Germany.
2022
Chromik, J.*, Kirsten, K.*, Herdick, A., Kappattanavar, A. M., & Arnrich, B. (2022). SensorHub: Multimodal sensing in real life enables home-based studies. Sensors, 22(1), 408. https://doi.org/10.3390/s22010408
Joch, J.*, Kirsten, K.*, & Arnrich, B. (2022). Hand gesture recognition in daily life as an additional tool for unobtrusive data labeling in medical studies. In Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (iWOAR ’22). Association for Computing Machinery.
Kirsten, K., & Arnrich, B. (2022). Elements of a system for automatic monitoring of specific mental health characteristics at home. In Proceedings of the 25th International Multiconference Information Society 2022 (IS 2022), Ljubljana, Slovenia.
2021
Kirsten, K.*, Pfitzner, B.*, Löper, L., & Arnrich, B. (2021). Sensor-based obsessive-compulsive disorder detection with personalised federated learning. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 333–339). IEEE. https://doi.org/10.1109/ICMLA52953.2021.00058
* indicating shared first authorship
personal
- since April 2019: Research Assistant and PhD Candidate
- 2017 - 2019: IT-Consultant & Full-Stack Software Developer at PricewaterhouseCoopers GmbH, Berlin
- 2013 - 2017: IT Systems Engineering (M.Sc.), Hasso Plattner Institute, Potsdam
- 2010 - 2013: International Media and Computing (B.Sc.), University of Applied Sciences, Berlin