Information and communication technology plays an increasingly important role in the healthcare system. Digital Health research takes place at the interface between healthcare, social sciences and digitalisation – among the results are healthcare apps and improved methods for research, diagnosis, therapy and rehabilitation.
DICHTE - Digitised Interprofessional Collaboration of Health Teams in Education
Digitised methods that support academic staff in organising person-centred, interprofessional health care education.
Applied Biomechanics in Rehabilitation Research
Endowed professorship of the province of Lower Austria to strengthen and expand the research focus on motor rehabilitation
Smart Companion 2
Equipping devices for everyday use with voice interaction, fall detection and alerts as support in cases of emergency.
E+DIETing_LAB- Digital Lab for Education in Dietetics combining Experiential Learning and Community Service
New ways of educating and upskilling teachers, students and experts in the field of dietetics.
O3DGA – The Use of Machine Learning as a Supportive Measure in Clinical Three-Dimensional Gait Analysis
Optimisation of time-consuming and error-prone processes in three-dimensional gait analysis.
UASHome - DIGIHealth UASHome Incubators Boost Programme
Promoting University of Applied Sciences as innovation and entrepreneurship centres for Digital Health
FIVE - #Fitspiration Image VErification
Proof of Concept of an interdisciplinary online-course offering guidance on how to deal critically with information provided by social media sites.
immtaCare – Immersive Technology-Assisted Nursing Education and Training
Extended Reality (XR) methods for trainings in home-based care
INPRO - ¡nterprofessionalism in action!
Aligning interprofessional education and collaboration in practice, using promising regional experiences for international exchange
Höld, E., Grüblbauer, J., Wiesholzer, M., Wewerka-Kreimel, D., Stieger, S., Kuschei, W., Kisser, P., Gützer, E., Hemetek, U., Ebner-Zarl, A., & Pripfl, J. (2022). Improving glycemic control in patients with type 2 diabetes mellitus through a peer support instant messaging service intervention (DiabPeerS): study protocol for a randomized controlled trial. Trials, 23(1), 308. https://doi.org/10.1186/s13063-022-06202-2
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
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
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
Schwab, C., Durstberger, S., Kainz, H., Baca, A., Thajer, A., Greber-Platzer, S., Ilse, J., Horsak, B., & Kranzl, A. (2021). Accuracy of 3-dimensional freehand ultrasound to estimate anatomical landmarks in children and adolescents with obesity. Gait & Posture, 90, 232–233. https://doi.org/https://doi.org/10.1016/j.gaitpost.2021.09.120
Horsak, B., Simonlehner, M., Schöffer, L., Dumphart, B., Jalaeefar, A., & Husinsky, M. (2021). Overground walking in a fully immersive Virtual Reality: Preliminary results of a comprehensive study on the effects on walking biomechanics. Gait & Posture, 90, 100–101. https://doi.org/https://doi.org/10.3389/fbioe.2021.780314
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
Aigner, W. (2021, March 23). Visual Analytics for Time-Oriented Data [Invited Talk]. ERFA Industrial Data & Analytics - Zeitreihenanalyse, Kremsmünster, Austria.
Jandl, C., Taurer, F., Hartner-Tiefenthaler, M., Wagner, M., Moser, T., & Schlund, S. (2021). Perceptions of Using Tracking and Tracing Systems in Work Environments. In F. F.-H. Nah & K. Siau (Eds.), HCI in Business, Government and Organizations (Vol. 12783, pp. 384–398). Springer International Publishing. https://doi.org/10.1007/978-3-030-77750-0_24