@inproceedings{krondorfer_deep_2021, series = {{ESMAC} 2021 {Abstracts}}, title = {Deep learning-based similarity retrieval in clinical {3D} gait analysis}, volume = {90}, url = {https://www.sciencedirect.com/science/article/pii/S0966636221003751}, doi = {https://doi.org/10.1016/j.gaitpost.2021.09.066}, language = {en}, urldate = {2021-10-15}, booktitle = {Gait \& {Posture}}, author = {Krondorfer, P. and Slijepčević, D. and Unglaube, F. and Kranzl, A. and Breiteneder, C. and Zeppelzauer, M. and Horsak, B.}, month = oct, year = {2021}, note = {Projekt: I3D}, keywords = {Biomechanics, Center for Artificial Intelligence, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Gait Analysis, Gait Classification, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, SP CDHSI Motor Rehabilitation, Vortrag, Wiss. Beitrag, best, peer-reviewed}, pages = {127--128}, }