Romijnders, R., Salis, F., Hansen, C., Küderle, A., Paraschiv-Ionescu, A., Cereatti, A., Alcock, L., Aminian, K., Becker, C., Bertuletti, S., Bonci, T., Brown, P., Buckley, E., Cantu, A., Carsin, A.-E., Caruso, M., Caulfield, B., Chiari, L., D’Ascanio, I., Del Din, S., Eskofier, B., Johansson Fernstad, S., Fröhlich, M. S., Garcia Aymerich, J., Gazit, E., Hausdorff, J. M., Hiden, H., Hume, E., Keogh, A., Kirk, C., Kluge, F., Koch, S., Mazzà, C., Megaritis, D., Micó-Amigo, E., Müller, A., Palmerini, L., Rochester, L., Schwickert, L., Scott, K., Sharrack, B., Singleton, D., Soltani, A., Ullrich, M., Vereijken, B., Vogiatzis, I., Yarnall, A., Schmidt, G., Maetzler, W. (2023). Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Frontiers in Neurology, 14, 1247532. https://doi.org/10.3389/fneur.2023.1247532
