@article{slijepcevic_input_2020, title = {Input {Representations} and {Classification} {Strategies} for {Automated} {Human} {Gait} {Analysis}}, volume = {76}, issn = {0966-6362}, doi = {10/ghz24x}, journal = {Gait \& Posture}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Breitender, Christian and Horsak, Brian}, year = {2020}, note = {Projekt: IntelliGait Projekt: I3D Projekt: ReMoCap-Lab Projekt: DHLab}, keywords = {2020, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed}, pages = {198--203}, } @article{wagner_kavagait_2018, title = {{KAVAGait}: {Knowledge}-{Assisted} {Visual} {Analytics} for {Clinical} {Gait} {Analysis}}, volume = {25}, url = {https://doi.org/10.1109/TVCG.2017.2785271}, doi = {10/ghppzn}, abstract = {In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.}, number = {3}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {Wagner, Markus and Slijepcevic, Djordje and Horsak, Brian and Rind, Alexander and Zeppelzauer, Matthias and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Design Study, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Healthcare, Human Gait Analysis, Human-Computer Interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Visual analytics, Wiss. Beitrag, best, best-bhorsak, best-lbaigner, best-lbwagnerm, best-mzeppelzauer, information visualization, knowledge generation, peer-reviewed}, pages = {1528--1542}, } @article{slijepcevic_automatic_2018, title = {Automatic {Classification} of {Functional} {Gait} {Disorders}}, volume = {5}, issn = {2168-2194}, url = {https://arxiv.org/abs/1712.06405}, doi = {10/ghz24w}, number = {22}, urldate = {2017-12-21}, journal = {IEEE Journal of Biomedical and Health Informatics}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Schwab, Caterine and Schuller, Michael and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed}, pages = {1653 -- 1661}, } @inproceedings{horsak_explainable_2020, address = {München, Deutschland}, title = {Explainable {Artificial} {Intelligence} ({XAI}) und ihre {Anwendung} auf {Klassifikationsprobleme} in der {Ganganalyse}}, booktitle = {Abstractband des 3. {GAMMA} {Kongress}}, author = {Horsak, Brian and Dumphart, Bernhard and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: ReMoCap-Lab Projekt: DHLab Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Eintrag überprüfen, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @article{horsak_gaitrec_2020, title = {{GaitRec}, a large-scale ground reaction force dataset of healthy and impaired gait}, volume = {7:143}, copyright = {CC BY}, url = {https://www.nature.com/articles/s41597-020-0481-z}, doi = {10/gh372d}, number = {1}, journal = {Scientific Data}, author = {Horsak, Brian and Slijepcevic, Djordje and Raberger, Anna-Maria and Schwab, Caterine and Worisch, Marianne and Zeppelzauer, Matthias}, year = {2020}, note = {Projekt: I3D Projekt: IntelliGait Projekt: DHLab}, keywords = {2019, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, DHLab, Eintrag überprüfen, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Green OA, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Open Access, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, best-mzeppelzauer, peer-reviewed, submitted}, pages = {1--8}, } @inproceedings{schwab_intelligait_2018, address = {Hamburg, Deutschland}, title = {{IntelliGait}: {Automatische} {Gangmusteranalyse} für die robuste {Erkennung} von {Gangstörungen}}, booktitle = {Tagungsband des 2ten {GAMMA} {Kongress} ({Gesellschaft} für die {Analyse} {Menschlicher} {Motorik} in ihrer klinischen {Anwendung})}, author = {Schwab, Caterine and Slijepcevic, Djordje and Zeppelzauer, Matthias and Raberger, Anna-Maria and Dumphart, Bernhard and Baca, Arnold and Breitender, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, } @inproceedings{slijepcevic_ground_2017, address = {Trondheim, Norway}, title = {Ground reaction force measurements for gait classification tasks: {Effects} of different {PCA}-based representations}, volume = {57}, url = {http://www.gaitposture.com/article/S0966-6362(17)30712-9/pdf}, doi = {10.1016/j.gaitpost.2017}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Horsak, Brian and Schwab, Caterine and Raberger, Anna-Maria and Schüller, Michael and Baca, Arnold and Breitender, Christian and Zeppelzauer, Matthias}, year = {2017}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {2017, Biofeedback, Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Creative Industries, DHLab, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Präsentation, Publikationstyp Schriftpublikation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, project\_carma, project\_intelligait, ⚠️ Invalid DOI}, pages = {4--5}, } @inproceedings{slijepcevic_towards_2018, address = {Prague, Czech Republic}, title = {Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements.}, volume = {65}, doi = {10/gh38wn}, booktitle = {Gait \& {Posture} {Supplement}}, author = {Slijepcevic, Djordje and Zeppelzauer, Matthias and Schwab, Caterine and Raberger, Anna-Maria and Dumphart, B and Baca, Arnold and Breiteneder, Christian and Horsak, Brian}, year = {2018}, note = {Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, Classification, DHLab, FH SP Data Analytics \& Visual Computing, Feature Representations, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Gait Recognition, Human Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, PCA, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, SVM, Wiss. Beitrag, best, best-bhorsak, pattern recognition, peer-reviewed}, } @inproceedings{slijepcevic_usefullness_2019, address = {Vienna, Austria}, title = {On the usefullness of statistical parameter mapping for feature selection in automated gait classification}, booktitle = {Book of {Abstracts} of the 25th {Conference} of the {European} {Society} of {Biomechanics} ({ESB})}, author = {Slijepcevic, Djordje and Raberger, Anna-Maria and Zeppelzauer, Matthias and Dumphart, Bernhard and Breiteneder, Christian and Horsak, Brian}, year = {2019}, note = {Projekt: IntelliGait Projekt: DHLab}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health and Social Innovation, DHLab, Digital Health, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @misc{slijepcevic_explanation_2020, address = {Wien}, type = {Invited {Talk}}, title = {Explanation of {Automatic} {Predictions} in {Human} {Gait} {Analysis}}, language = {English}, author = {Slijepcevic, Djordje}, month = feb, year = {2020}, note = {Projekt: I3D}, keywords = {Computer Vision, Department Medien und Digitale Technologien, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Invited Talk, Machine Learning, Media Computing Group, Präsentation, Publikationstyp Vortrag, Wiss. Beitrag}, } @inproceedings{kirchknopf_multimodal_2021, address = {Lille, France}, title = {Multimodal {Detection} of {Information} {Disorder} from {Social} {Media}}, url = {https://ieeexplore.ieee.org/document/9461898}, doi = {10/gmxnm5}, abstract = {Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.}, language = {en}, urldate = {2021-07-12}, booktitle = {International {Conference} on {Content}-{Based} {Multimedia} {Indexing} ({CBMI})}, publisher = {IEEE}, author = {Kirchknopf, Armin and Slijepcevic, Djordje and Zeppelzauer, Matthias}, year = {2021}, note = {arXiv: 2105.15165}, keywords = {Artificial Intelligence, Center for Artificial Intelligence, Digital Media, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Poster, Wiss. Beitrag, best-akirchknopf, best-mzeppelzauer, peer-reviewed}, pages = {4}, } @inproceedings{strebl_how_2020, address = {Graz (Austria)}, title = {How {High} is the {Tide}? {Estimation} of {Flood} {Level} from {Social} {Media}}, doi = {10/gnt2wh}, booktitle = {Proceedings of {Joint} {Austrian} {Computer} {Vision} and {Robotics} {Workshop} 2020}, publisher = {TU Graz}, author = {Strebl, Julia and Slijepcevic, Djordje and Kirchknopf, Armin and Sakeena, Muntaha and Seidl, Markus and Zeppelzauer, Matthias}, year = {2020}, keywords = {Center for Artificial Intelligence, Computer Vision, Data Science, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Open Access, Visual Computing, Wiss. Beitrag, peer-reviewed}, pages = {2}, } @inproceedings{strebl_flood_2019, address = {Nice, France}, title = {Flood {Level} {Estimation} from {Social} {Media} {Images}}, volume = {2670}, booktitle = {{CEUR} {Proceedings} of the {MediaEval} 2019 {Workshop}}, author = {Strebl, Julia and Slijepcevic, Djordje and Kirchknopf, Armin and Sakeena, Muntaha and Seidl, Markus and Zeppelzauer, Matthias}, year = {2019}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Vortrag, Wiss. Beitrag, ⛔ No DOI found}, pages = {2}, } @inproceedings{kirchknopf_detection_2018, address = {Sophia Antipolis, France}, title = {Detection of {Road} {Passability} from {Social} {Media} and {Satellite} {Images}}, abstract = {This paper presents the contribution of Team MC-FHSTP to the multimedia satellite task at the MediaEval 2018 benchmark. We present two methods, one for the estimation of the passability of roads from social media images due to flooding and one method that estimates passability from satellite images. We present the results obtained in the benchmark for both methods.}, booktitle = {{CEUR} {Proceedings} of the {MediaEval} 2018 {Workshop}}, author = {Kirchknopf, Armin and Slijepcevic, Djordje and Zeppelzauer, Matthias and Seidl, Markus}, year = {2018}, keywords = {Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Vortrag, Wiss. Beitrag, ⛔ No DOI found}, pages = {2}, }