Publications: Research Group Media Computing - STP UAS

Dumphart, B., Djordje, S., Unglaube, F., Kranzl, A., Baca, A., & Horsak, B. (2024). The impact of initial contact events on kinematics in pathological gait - Preliminary results of an ongoing study. Gait & Posture, 113, 54–55. https://doi.org/10.1016/j.gaitpost.2024.07.067

Horst, F., Slijepcevic, D., Schöllhorn, W. I., Horsak, B., & Zeppelzauer, M. (2024). Explainable artificial intelligence for walking speed classification from vertical ground reaction forces. Gait & Posture, 113, 215–216. https://doi.org/10.1016/j.gaitpost.2024.07.232

Slijepcevic, D., Krondorfer, P., Unglaube, F., Kranzl, A., Zeppelzauer, M., & Horsak, B. (2024). Predicting ground reaction forces in overground walking from gait kinematics using machine learning. Gait & Posture, 113, 214–215. https://doi.org/10.1016/j.gaitpost.2024.07.231

Vulpe-Grigorasi, A., Kren, Z., Slijepčević, D., Schmied, R., & Leung, V. (2024). Attention performance classification based on eye tracking and machine learning. Informatics 2024 Proceedings. IEEE International Scientific Conference on Informatics, Poprad Slovakia. https://doi.org/10.1109/Informatics62280.2024.10900810

Bruckner, Franziska, Feyersinger, Erwin, & Lechner, Patrik. (2023, June 14). AniVision: Machine Learning as a Tool for Studying Animation in Ephemeral Films [Vortrag]. Society for Animation Studies 34th Annual Conference – The Animated Environment, Online – Glassboro. https://research.fhstp.ac.at/projekte/anivision

de Jesus Oliveira, V. A., Slijepčević, D., Dumphart, B., Ferstl, S., Reis, J., Raberger, A.-M., Heller, M., Horsak, B., & Iber, M. (2023). Auditory feedback in tele-rehabilitation based on automated gait classification. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-023-01723-2

Dumphart, B., Slijepcevic, D., Zeppelzauer, M., Kranzl, A., Unglaube, F., Baca, A., & Horsak, B. (2023). Robust deep learning-based gait event detection across various pathologies. PLOS ONE, 18(8), e0288555. https://doi.org/10.1371/journal.pone.0288555

Dumphart, B., Slijepcevic, D., Kranz, A., Zeppelzauer, M., & Horsak, B. (2023). Is it time to re-think the appropriateness of autocorrelation for gait event detection? Preliminary results of an ongoing study. Gait & Posture, 106, S50–S51. https://doi.org/10.1016/j.gaitpost.2023.07.064

Horst, F., Slijepcevic, D., Simak, M., Horsak, B., Schöllhorn, W. I., & Zeppelzauer, M. (2023). Modeling biological individuality using machine learning: A study on human gait. Computational and Structural Biotechnology Journal, 21, 3414–3423. https://doi.org/10.1016/j.csbj.2023.06.009

Bibtex

Killian, S., Baumann, C., Delorette, M., Freisleben- Teutscher, C., Größbacher, S., Huber, A., Husinsky, M., Judmair, P., Moser, T., Pflegerl, J., Schlager, A., Schöffer, L., Taurer, F., & Vogt, G. (2023). MIRACLE - Mixed Reality und Cooperation im Lehreinsatz: Erfahrungen, Potenziale, Limitationen. In Lernen über den Tellerrand hinaus. Good Practices zu Interdisziplinarität, Internationalisierung und Future Skills (pp. 119–126). Lemberger Publishing.

Bibtex

Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy. IEEE Access, 11, 65906–65923. https://doi.org/10.1109/ACCESS.2023.3289986

Slijepcevic, D., Horst, F., Simak, M., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2023). Towards personalized gait rehabilitation: How robustly can we identify personal gait signatures with machine learning? Gait & Posture, 106, S192–S193. https://doi.org/10.1016/j.gaitpost.2023.07.232

Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy. Gait & Posture, 100, 32–33. https://doi.org/10.1016/j.gaitpost.2022.11.045

Luh, R., Eresheim, S., Größbacher, S., Petelin, T., Mayr, F., Tavolato, P., & Schrittwieser, S. (2022). PenQuest Reloaded: A Digital Cyber Defense Game for Technical Education. 2022 IEEE Global Engineering Education Conference (EDUCON), 906–914. https://doi.org/10.1109/EDUCON52537.2022.9766700

Rind, A., Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., & Horsak, B. (2022). Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. Proc. 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), 7–15. https://doi.org/10.1109/TREX57753.2022.00006

Slijepcevic, D., Horst, F., Simak, M., Lapuschkin, S., Raberger, A. M., Samek, W., Breiteneder, C., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2022). Explaining machine learning models for age classification in human gait analysis. Gait & Posture, 97, S252–S253. https://doi.org/10.1016/j.gaitpost.2022.07.153

Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breitender, C., Schöllhorn, W., & Zeppelzauer, M. (2022). Explaining Machine Learning Models for Clinical Gait Analysis. ACM Transactions on Computing for Healthcare, 3(2), 14:1-14:27. https://doi.org/10.1145/3474121

Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2021). A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–42. https://doi.org/10/gnt2wf

Dumphart, B., Slijepčević, D., Unglaube, F., Kranzl, A., Baca, A., Zeppelzauer, M., & Horsak, B. (2021). An automated deep learning-based gait event detection algorithm for various pathologies. Gait & Posture, 90, 50–51. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.026

Horsak, B., Simonlehner, M., Schöffer, L., Dumphart, B., Jalaeefar, A., & Husinsky, M. (2021). Overground Walking in a Fully Immersive Virtual Reality: A Comprehensive Study on the Effects on Full-Body Walking Biomechanics. Frontiers in Bioengineering and Biotechnology, 9, 1236. https://doi.org/https://doi.org/10.3389/fbioe.2021.780314