@inproceedings{slijepcevic_usefulness_2019, address = {Vienna, Austria}, title = {On the usefulness 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 Innovation, 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 IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Vortrag, Wiss. Beitrag, best, best-bhorsak, peer-reviewed, ⛔ No DOI found}, pages = {1}, } @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}, } @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_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}, } @inproceedings{horsak_walking_2022, address = {Taipei, Taiwan}, title = {Walking overground in a room-scale {Virtual} {Reality} {Environment}: a motor control perspective}, author = {Horsak, Brian and Simonlehner, Mark and Schöffer, Lucas and Dumphart, Bernhard and Jalaeefar, Arian and Husinsky, Matthias and Siragy, Tarique}, year = {2022}, keywords = {Biofeedback, Biomechanics, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Virtual Reality, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, } @article{horsak_overground_2021, title = {Overground {Walking} in a {Fully} {Immersive} {Virtual} {Reality}: {A} {Comprehensive} {Study} on the {Effects} on {Full}-{Body} {Walking} {Biomechanics}}, volume = {9}, copyright = {CC-BY}, issn = {2296-4185}, shorttitle = {Overground {Walking} in a {Fully} {Immersive} {Virtual} {Reality}}, url = {https://www.frontiersin.org/article/10.3389/fbioe.2021.780314}, doi = {https://doi.org/10.3389/fbioe.2021.780314}, abstract = {Virtual reality (VR) is an emerging technology offering tremendous opportunities to aid gait rehabilitation. To this date, real walking with users immersed in virtual environments with head-mounted displays (HMDs) is either possible with treadmills or room-scale (overground) VR setups. Especially for the latter, there is a growing interest in applications for interactive gait training as they could allow for more self-paced and natural walking. This study investigated if walking in an overground VR environment has relevant effects on 3D gait biomechanics. A convenience sample of 21 healthy individuals underwent standard 3D gait analysis during four randomly assigned walking conditions: the real laboratory (RLab), a virtual laboratory resembling the real world (VRLab), a small version of the VRlab (VRLab−), and a version which is twice as long as the VRlab (VRLab+). To immerse the participants in the virtual environment we used a VR-HMD, which was operated wireless and calibrated in a way that the virtual labs would match the real-world. Walking speed and a single measure of gait kinematic variability (GaitSD) served as primary outcomes next to standard spatio-temporal parameters, their coefficients of variant (CV\%), kinematics, and kinetics. Briefly described, participants demonstrated a slower walking pattern (−0.09 ± 0.06 m/s) and small accompanying kinematic and kinetic changes. Participants also showed a markedly increased gait variability in lower extremity gait kinematics and spatio-temporal parameters. No differences were found between walking in VRLab+ vs. VRLab−. Most of the kinematic and kinetic differences were too small to be regarded as relevant, but increased kinematic variability (+57\%) along with increased percent double support time (+4\%), and increased step width variability (+38\%) indicate gait adaptions toward a more conservative or cautious gait due to instability induced by the VR environment. We suggest considering these effects in the design of VR-based overground training devices. Our study lays the foundation for upcoming developments in the field of VR-assisted gait rehabilitation as it describes how VR in overground walking scenarios impacts our gait pattern. This information is of high relevance when one wants to develop purposeful rehabilitation tools.}, urldate = {2021-12-03}, journal = {Frontiers in Bioengineering and Biotechnology}, author = {Horsak, Brian and Simonlehner, Mark and Schöffer, Lucas and Dumphart, Bernhard and Jalaeefar, Arian and Husinsky, Matthias}, year = {2021}, keywords = {Biofeedback, Biomechanics, Center for Digital Health and Social Innovation, Department Gesundheit, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Gait Analysis, Institut für Gesundheitswissenschaften, SP CDHSI Motor Rehabilitation, Virtual Reality, Wiss. Beitrag, best, peer-reviewed}, pages = {1236}, } @inproceedings{iber_mind_2021, title = {Mind the {Steps}: {Towards} {Auditory} {Feedback} in {Tele}-{Rehabilitation} {Based} on {Automated} {Gait} {Classification}}, doi = {10/gnt2tc}, abstract = {We describe a proof-of-concept for the implementation of a mobile auditory biofeedback system based on automated classification of functional gait disorders. The classification is embedded in a sensor-instrumented insole and is based on ground reaction forces (GRFs). GRF data have been successfully used for the classification of gait patterns into clinically relevant classes and are frequently used in clinical practice to quantitatively describe human motion. A feed-forward neural network that was implemented on the firmware of the insole is used to estimate the GRFs using pressure and accelerator data. Compared to GRF measurements obtained from force plates, the estimated GRFs performed highly accurately. To distinguish between physiological gait and gait disorders, we trained and evaluated a support vector machine with labeled data from a publicly accessible database. The automated gait classification was sonified for auditory feedback. The high potential of the implemented auditory feedback for preventive and supportive applications in physical therapy, such as supervised therapy settings and tele-rehabilitation, was highlighted by a semi- structured interview with two experts.}, booktitle = {In {Proceedings} of the 16th {International} {Audio} {Mostly} {Conference} ({AM}’21)}, publisher = {ACM}, author = {Iber, Michael and Dumphart, Bernhard and Oliveira, Victor A. de. J. and Ferstl, Stefan and Reis, Joschua and Slijepcevic, Djordje and Heller, Mario and Raberger, Anna-Maria and Horsak, Brian}, year = {2021}, note = {Projekt: Sonigait II}, keywords = {Artificial Intelligence, Biofeedback, Biomechanics, CDHI, Digital Health, Forschungsgruppe Media Computing, Gait Analysis, Human-computer interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Motor rehabilitation, Vortrag, Wiss. Beitrag, best, best-lbiber, peer-reviewed}, } @inproceedings{dumphart_validity_2021, address = {Virtual Meeting}, title = {Validity and reliability of a mobile insole to measure vertical ground reaction force during walking}, author = {Dumphart, Bernhard and Schimakno, Markus and Nöstlinger, Stefan and Iber, Michael and Horsak, Brian and Heller, Mario}, year = {2021}, note = {Projekt: DHLab Project: SONIGAIT II}, keywords = {Biofeedback, Biomechanics, Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Department Gesundheit, Gait Analysis, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Publikationstyp Präsentation, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Vortrag, Wiss. Beitrag, best, peer-reviewed, ⛔ No DOI found}, }