Brian Horsak

Endowed Professor for Applied Biomechanics and Rehabilitation Research

St. Pölten University of Applied Sciences

Austria

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Brian Horsak is an Endowed Professor for Applied Biomechanics and Rehabilitation Research at St. Pölten University of Applied Sciences, Austria. His research focuses on various aspects of gait analysis, body composition, and motion capture technologies, with recent publications examining the reliability of markerless gait analysis and the impact of virtual reality on dynamic balance. His work integrates machine learning and biomechanics to bridge the gap between laboratory research and real-world applications in movement analysis.

Articles (18)

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2025

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2023

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2022

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2022

Collaborators (6)

Sebastian Lapuschkin

Head of Explainable AI Group

Fraunhofer-Institut für Nachrichtentechnik Heinrich-Hertz-Institut

GERMANY

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Andreas Kranzl

head of the laboratory

AUSTRIA

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Matthias Zeppelzauer

St. Pölten University of Applied Sciences

AUSTRIA

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Hans Kainz

Assistant professor

Universität Wien

AUSTRIA

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Susanne Greber-Platzer

Prof. Dr.

Medical University of Vienna

AUSTRIA

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Wolfgang Schoellhorn

Univ. Prof. Dr.

Johannes Gutenberg Universität Mainz

GERMANY

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Brian Horsak

Research Interests

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Articles (18)

Comparing Bioelectrical Impedance Analysis, Air Displacement Plethysmography, and Dual-Energy X-Ray Absorptiometry for Body Composition in Pediatric Obesity

Validity and reliability of monocular 3D markerless gait analysis in simulated pathological gait: A comparative study with OpenCap

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis

Decoding Gait Signatures: Exploring Individual Patterns in Pathological Gait Using Explainable AI

Mediolateral Margin of Stability highlights motor strategies for maintaining dynamic balance in older adults

GaitRec-VR: 3D Gait Analysis for Walking Overground with and without a Head-Mounted-Display in Virtual Reality

From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary

Overground walking while using a virtual reality head mounted display increases variability in trunk kinematics and reduces dynamic balance in young adults

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait

Robust deep learning-based gait event detection across various pathologies

Different walking strategies impact patella cartilage pressure in individuals with patellofemoral instability

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

Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy

Modeling biological individuality using machine learning: A study on human gait

Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy

Unraveling the effects of virtual reality overground walking on dynamic balance and postural control

Explaining Machine Learning Models for Clinical Gait Analysis

Collaborators (6)

Sebastian Lapuschkin

Andreas Kranzl

Matthias Zeppelzauer

Hans Kainz

Susanne Greber-Platzer

Wolfgang Schoellhorn

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