Peer-Reviewed Publication
Knee Surg Sports Traumatol Arthrosc2026September 29, 2026Journal Article
Detection of previous knee injuries using lower limb-worn inertial measurement units: A cross-sectional machine-learning study.
Christina Valle1,2, Matthias Cotic1, Maximilian Bartelt1, Luca Seeberger1, Birgit Waschulzik3, Sebastian Siebenlist1, Michael T Hirschmann4,5, Georg Matziolis6, Rüdiger von Eisenhart-Rothe1, Daniel Rueckert3,7, Florian Hinterwimmer1,3, Ricardo Smits Serena1,3
1Department of Orthopaedics and Sports Orthopaedics, TUM University Hospital, Munich, Germany.
2Clinic for Orthopaedic Rehabilitation, Medical Park Chiemsee, Bernau-Felden, Germany.
3Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.
4Department of Orthopedic Surgery and Traumatology, Kantonsspital Baselland, Bruderholz, Switzerland.
5Department of Clinical Research, Research Group Michael T. Hirschmann, Regenerative Medicine and Biomechanics, University of Basel, Basel, Switzerland.
6Orthopaedics, Campus Waldkliniken Eisenberg, University Hospital Jena, Friedrich Schiller University, Jena, Germany.
7Department of Computing, Imperial College London, South Kensington Campus, London, UK.
Abstract
PURPOSE: Previous knee injury increases the risk of subsequent injury, even after functional recovery, yet residual gait, running or load alterations often go undetected in routine assessment. Wearable inertial measurement units (IMUs) may enable scalable detection of such alterations outside laboratory settings. The primary aim of this study was to determine whether IMU-derived gait and running f…
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