Institut für
Creative\Media/Technologies

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Dipl.-Ing. Djordje Slijepčević BSc

  • Researcher
    Forschungsgruppe Media Computing
    Institut für Creative\Media/Technologies
  • Department Medien und Digitale Technologien
Arbeitsplatz: A - Campus-Platz 1
T: +43/2742/313 228 654

Studiengänge

  • Creative Computing (BA)
  • Data Intelligence (MA)
  • Digital Design (MA)
  • Medientechnik (BA)
  • Interactive Technologies (MA)
  • Data Science and Artificial Intelligence* (BA)
  • Digital Media Production (MA)
  • Digital Business Communications (MA)

Departments

  • Informatik und Security
  • Digital Business und Innovation
  • Medien und Digitale Technologien

Publikationen

Bibtex für alle Ergebnisse downloaden
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
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
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
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
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
Horst, F., Hoitz, F., Slijepcevic, D., Schons, N., Beckmann, H., Nigg, B. M., & Schöllhorn, W. I. (2023). Identification of subject-specific responses to footwear during running. Scientific Reports, 13(1), 11284. https://doi.org/10.1038/s41598-023-38090-0
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
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
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., 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
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
Baumhauer, T., Slijepcevic, D., & Zeppelzauer, M. (2022). Bounded logit attention: Learning to explain image classifiers. NeurIPS 2022 Workshop: All Things Attention: Bridging Different Perspectives on Attention, New Orleans, USA. https://arxiv.org/pdf/2105.14824
Kirchknopf, A., Slijepcevic, D., Wunderlich, I., Breiter, M., Traxler, J., & Zeppelzauer, M. (2021). Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections. Proceedings of the Workshop of the Austrian Association for Pattern Recognition, 3. https://doi.org/10.3217/978-3-85125-869-1-13
Horst, F., Slijepcevic, D., Simak, M., & Schöllhorn, W. I. (2021). Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals. Scientific Data, 8(1), 232. https://doi.org/https://doi.org/10.1038/s41597-021-01014-6
Iber, M., Dumphart, B., Oliveira, V. A. de. J., Ferstl, S., Reis, J., Slijepcevic, D., Heller, M., Raberger, A.-M., & Horsak, B. (2021). Mind the Steps: Towards Auditory Feedback in Tele-Rehabilitation Based on Automated Gait Classification. In Proceedings of the 16th International Audio Mostly Conference (AM"21). Audio Mostly 2021. https://doi.org/10/gnt2tc
Kirchknopf, A., Slijepcevic, D., & Zeppelzauer, M. (2021). Multimodal Detection of Information Disorder from Social Media. International Conference on Content-Based Multimedia Indexing (CBMI), 4. https://doi.org/10/gmxnm5
Boeck, J., Liakhovets, D., Schütz, M., Kirchknopf, A., Slijepcevic, D., Zeppelzauer, M., & Schindler, A. (2021). AIT_FHSTP at GermEval 2021: Automatic Fact Claiming Detection with Multilingual Transformer Models. Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, 76–82. https://aclanthology.org/2021.germeval-1.11.pdf
Slijepcevic, D. (2020, February 19). Explanation of Automatic Predictions in Human Gait Analysis [Invited Talk]. Explainable AI Workshop, TU Wien, Wien.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., Worisch, M., & Zeppelzauer, M. (2020). GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Scientific Data, 7:143(1), 1–8. https://doi.org/10/gh372d
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. Abstractband Des 3. GAMMA Kongress. 3. GAMMA Kongress, München, Deutschland.
Strebl, J., Slijepcevic, D., Kirchknopf, A., Sakeena, M., Seidl, M., & Zeppelzauer, M. (2020). How High is the Tide? Estimation of Flood Level from Social Media. Proceedings of Joint Austrian Computer Vision and Robotics Workshop 2020, 2. https://doi.org/10/gnt2wh
Slijepcevic, D., Zeppelzauer, M., Schwab, Caterine, Raberger, A.-M., Breitender, C., & Horsak, B. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203. https://doi.org/10/ghz24x
Vyssoki, S., Stoiber, C., Slijepcevic, D., Wagner-Havlicek, C., Wagner, M., & Wagner, M. (2019, October 17). Kompetenzorientierte Prüfungsdesigns - DataBootCamp und Parallel Escape Rooms. Gelernt Wird, Was Geprüft Wird", Oder…?! Assessment in Der Hochschullehre Neu Denken: Good Practices – Herausforderungen – Visionen. 8. Tag der Lehre, St. Pölten. http://skill.fhstp.ac.at/wp-content/uploads/2019/11/Tagungsband_2019.pdf
Slijepcevic, D., Raberger, A.-M., Zeppelzauer, M., Dumphart, B., Breiteneder, C., & Horsak, B. (2019). On the usefulness of statistical parameter mapping for feature selection in automated gait classification. Book of Abstracts of the 25th Conference of the European Society of Biomechanics (ESB), 1.
Slijepcevic, D., Raberger, A.-M., Zeppelzauer, M., Dumphart, B., Breiteneder, C., & Horsak, B. (2019). On the usefullness of statistical parameter mapping for feature selection in automated gait classification. Book of Abstracts of the 25th Conference of the European Society of Biomechanics (ESB), 1.
Strebl, J., Slijepcevic, D., Kirchknopf, A., Sakeena, M., Seidl, M., & Zeppelzauer, M. (2019). Flood Level Estimation from Social Media Images. CEUR Proceedings of the MediaEval 2019 Workshop, 2670, 2.
Wagner, M., Slijepcevic, D., Horsak, B., Rind, A., Zeppelzauer, M., & Aigner, W. (2018). KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis. IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(3), 1528–1542. https://doi.org/10/ghppzn
Schwab, C., Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Dumphart, B., Baca, A., Breitender, C., & Horsak, B. (2018). IntelliGait: Automatische Gangmusteranalyse für die robuste Erkennung von Gangstörungen. Tagungsband Des 2ten GAMMA Kongress (Gesellschaft Für Die Analyse Menschlicher Motorik in Ihrer Klinischen Anwendung). 2ter GAMMA Kongress (Gesellschaft für die Analyse Menschlicher Motorik in ihrer klinischen Anwendung), Hamburg, Deutschland.
Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Schwab, C., Schuller, M., Baca, A., Breiteneder, C., & Horsak, B. (2018). Automatic Classification of Functional Gait Disorders. IEEE Journal of Biomedical and Health Informatics, 5(22), 1653–1661. https://doi.org/10/ghz24w
Kirchknopf, A., Slijepcevic, D., Zeppelzauer, M., & Seidl, M. (2018). Detection of Road Passability from Social Media and Satellite Images. CEUR Proceedings of the MediaEval 2018 Workshop, 2.
Slijepcevic, D., Zeppelzauer, M., Schwab, C., Raberger, A.-M., Dumphart, B., Baca, A., Breiteneder, C., & Horsak, B. (2018). Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements. Gait & Posture Supplement, 65. https://doi.org/10/gh38wn
Slijepcevic, D., Horsak, B., Schwab, C., Raberger, A.-M., Schüller, M., Baca, A., Breitender, C., & Zeppelzauer, M. (2017). Ground reaction force measurements for gait classification tasks: Effects of different PCA-based representations. Gait & Posture Supplement, 57, 4–5. https://doi.org/10.1016/j.gaitpost.2017

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