Publications: Research Group Media Computing - STP UAS

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

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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., 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/gnt2s9

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

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

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

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Krondorfer, P., Slijepčević, D., Unglaube, F., Kranzl, A., Breiteneder, C., Zeppelzauer, M., & Horsak, B. (2021). Deep learning-based similarity retrieval in clinical 3D gait analysis. Gait & Posture, 90, 127–128. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.066

Slijepčević, D., Henzl, M., Klausner, L. D., Dam, T., Kieseberg, P., & Zeppelzauer, M. (2021). k‑Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers. Computers & Security, 111, 19. https://doi.org/10.1016/j.cose.2021.102488

Zielinski, B., Lipinski, M., Juda, M., Zeppelzauer, Matthias, & Dlotko, Pawel. (2021). Persistence Codebooks for Topological Data Analysis. Journal of Artificial Intelligence Review, 54, 1969–2009. https://doi.org/https://doi.org/10.1007/s10462-020-09897-4

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.

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Horst, F., Slijepcevic, D., Zeppelzauer, M., Raberger, A. M., Lapuschkin, S., Samek, W., Schöllhorn, W. I., Breiteneder, C., & Horsak, B. (2020). Explaining automated gender classification of human gait. Gait & Posture, 81, supplement 1, 159–160. https://doi.org/10/ghr9k6

Iber, M., Lechner, P., Jandl, C., Mader, M., & Reichmann, M. (2020). Auditory augmented process monitoring for cyber physical production systems. Personal and Ubiquitous Computing. https://doi.org/10/ghz24q

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

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Despotovic, M., Koch, D., Leiber, S., Döller, M., Sakeena, M., & Zeppelzauer, M. (2019). Prediction and analysis of heating energy demand for detached houses by computer vision. Energy & Buildings, 193, 29–35. https://doi.org/10/fsxn

Iber, M., Lechner, P., Jandl, C., Mader, M., & Reichmann, M. (2019). Auditory Augmented Reality for Cyber Physical Production Systems. AudioMostly (AM"19). AudioMostly (AM"19), Nottingham, UNited Kingdom. https://doi.org/10.1145/3356590.3356600}

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