Sensor-based gait analysis distinguishes fallers from non-fallers in Parkinson's disease under clinical and real-life conditions

Gladow T, Gaßner H, Ullrich M, Hannink J, Roth N, Marxreiter F, Küderle A, Kohl Z, Winkler J, Eskofier B, Klucken J (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Journal of Neural Transmission Springer Nature

Book Volume: 126

Pages Range: 657-657

Journal Issue: 5

Authors with CRIS profile

Till Gladow Department of Molecular Neurology Heiko Gaßner Department of Molecular Neurology Martin Ullrich Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Julius Hannink Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Nils Roth Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Franz Marxreiter Department of Molecular Neurology Arne Küderle Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Zacharias Kohl Department of Molecular Neurology Jürgen Winkler Professur für Molekulare Neurologie Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Jochen Klucken Department of Molecular Neurology

Additional Organisation(s)

Machine Learning and Data Analytics Lab

How to cite

APA:

Gladow, T., Gaßner, H., Ullrich, M., Hannink, J., Roth, N., Marxreiter, F.,... Klucken, J. (2019). Sensor-based gait analysis distinguishes fallers from non-fallers in Parkinson's disease under clinical and real-life conditions. (pp. 657-657).

MLA:

Gladow, Till, et al. "Sensor-based gait analysis distinguishes fallers from non-fallers in Parkinson's disease under clinical and real-life conditions." 2019. 657-657.

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