• Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases 

      Romijnders, Robbin; Salis, Francesca; Hansen, Clint; Küderle, Arne; Paraschiv-Ionescu, Anisoara; Cereatti, Andrea; Alcock, Lisa; Aminian, Kamiar; Becker, Clemens; Bertuletti, Stefano; Bonci, Tecla; Brown, Philip; Buckley, Ellen; Cantu, Alma; Carsin, Anne-Elie; Caruso, Marco; Caulfield, Brian; Chiari, Lorenzo; D'Ascanio, Ilaria; Del Din, Silvia; Eskofier, Björn; Fernstad, Sara Johansson; Fröhlich, Marceli Stanislaw; Garcia Aymerich, Judith; Gazit, Eran; Hausdorff, Jeffrey M.; Hiden, Hugo; Hume, Emily; Keogh, Alison; Kirk, Cameron; Kluge, Felix; Koch, Sarah; Mazzà, Claudia; Megaritis, Dimitrios; Micó-Amigo, Encarna; Müller, Arne; Palmerini, Luca; Rochester, Lynn; Schwickert, Lars; Scott, Kirsty; Sharrack, Basil; Singleton, David; Soltani, Abolfazl; Ullrich, Martin; Vereijken, Beatrix; Vogiatzis, Ioannis; Yarnall, Alison; Schmidt, Gerhard; Maetzler, Walter (Peer reviewed; Journal article, 2023)
      Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting ...