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Alice Nieuwboer
Researcher at University of Copenhagen Faculty of Science
Publications - 12
Citations - 909
Alice Nieuwboer is an academic researcher from University of Copenhagen Faculty of Science. The author has contributed to research in topics: Gait (human) & Neural correlates of consciousness. The author has an hindex of 5, co-authored 12 publications receiving 830 citations.
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Journal Article
Cueing training in the home improves gait-related mobility in Parkinson's disease : the RESCUE trial. Commentary
Mark A. Hirsch,F. M. Hommond,Alice Nieuwboer,G. Kwakkel,Lynn Rochester,Derek K. Jones,E.E.H. van Wegen,A M Willems,F Chavret,V. Hetherington,Katherine Baker,I. Lim +11 more
TL;DR: The Rehabilitation in Parkinson's Disease: Strategies for Cueing (RESCUE) trial investigated the effects of a home physiotherapy program based on rhythmical cueing on gait and gaitrelated activity as discussed by the authors.
Virtual reality for rehabilitation in Parkinson’s disease: preliminary results of a systematic review
Kim Dockx,Veerle Van den Bergh,Esther M.J. Bekkers,Pieter Ginis,Sabine Verschueren,Lynn Rochester,Jeffrey M. Hausdorff,Anat Mirelman,Alice Nieuwboer +8 more
TL;DR: Low-quality evidence of a positive effect of short-term VR exercise on step and stride length is found, and VR and physiotherapy interventions may have similar effects on gait, balance, and quality of life.
Short Communication Concurrent validity of accelerometry to measure gait in Parkinsons Disease
TL;DR: In this paper, the concurrent validity of the VAM and GAITRite 1 to measure gait speed, step length and step frequency during a test of functional gait that included single, dual and multiple task components for 12 people with Parkinsons disease (PD) and 11 comparisons participating.
Journal ArticleDOI
Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.
Anat Mirelman,Anat Mirelman,Mor Ben Or Frank,Michal Melamed,Lena Granovsky,Alice Nieuwboer,Lynn Rochester,Silvia Del Din,Laura Avanzino,Elisa Pelosin,Bastiaan R. Bloem,Ugo Della Croce,Andrea Cereatti,Andrea Cereatti,Paolo Bonato,Richard Camicioli,Theresa Ellis,Jamie L. Hamilton,Chris J. Hass,Quincy J. Almeida,Maidan Inbal,Maidan Inbal,Avner Thaler,Avner Thaler,Julia C Shirvan,Jesse M. Cedarbaum,Nir Giladi,Nir Giladi,Jeffrey M. Hausdorff +28 more
TL;DR: In this paper, the authors identify the gait and mobility measures that are most sensitive and reflective of Parkinson's motor stages and determine the optimal sensor location in each disease stage by applying machine learning to multiple wearable-derived features.