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Abstract
Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual’s mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
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1 University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy (GRID:grid.6292.f) (ISNI:0000 0004 1757 1758); University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy (GRID:grid.6292.f) (ISNI:0000 0004 1757 1758)
2 University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy (GRID:grid.6292.f) (ISNI:0000 0004 1757 1758)
3 The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK (GRID:grid.11835.3e) (ISNI:0000 0004 1936 9262); The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK (GRID:grid.11835.3e) (ISNI:0000 0004 1936 9262)
4 Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212)
5 University of Sassari, Department of Biomedical Sciences, Sassari, Italy (GRID:grid.11450.31) (ISNI:0000 0001 2097 9138)
6 Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343); Politecnico di Torino, PolitoBIOMed Lab – Biomedical Engineering Lab, Torino, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343)
7 Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343)
8 Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel (GRID:grid.413449.f) (ISNI:0000 0001 0518 6922)
9 Ecole Polytechnique Federale de Lausanne, Laboratory of Movement Analysis and Measurement, Lausanne, Switzerland (GRID:grid.5333.6) (ISNI:0000000121839049)
10 Friedrich-Alexander-University Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Erlangen, Germany (GRID:grid.5330.5) (ISNI:0000 0001 2107 3311)
11 Newcastle University, School of Computing, Newcastle, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212)
12 University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy (GRID:grid.6292.f) (ISNI:0000 0004 1757 1758)
13 Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany (GRID:grid.412468.d) (ISNI:0000 0004 0646 2097)
14 Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212); The Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK (GRID:grid.1006.7)