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.
EyeQTrack - Quantitative Eye-Tracking Analytics for Adaptive XR Training & Rehabilitation in Healthcare
A self-adaptive eXtended reality environment for training and therapeutic purposes.
Digitised methods that support academic staff in organising person-centred, interprofessional health care education.
Endowed professorship of the province of Lower Austria to strengthen and expand the research focus on motor rehabilitation
deepForce - Pushing the limits of rapid estimation of knee joint contact force estimation in clinical gait analysis by Machine and Deep Learning
Machine learning methods to enable more clinicians to use data obtained by gait analyses for diagnosis.
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.
Promoting University of Applied Sciences as innovation and entrepreneurship centres for Digital Health
Proof of Concept of an interdisciplinary online-course offering guidance on how to deal critically with information provided by social media sites.
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
Durstberger, S., Kranzl, A., & Horsak, B. (2023). Effects of three different regression-based hip joint center localization methods in adolescents with obesity on kinematics and kinetics - preliminary results of the HIPstar study. Gait & Posture, 100, 42–43. https://doi.org/10.1016/j.gaitpost.2022.11.056
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
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
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
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
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: 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