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Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization.
Palmerini, Luca; Reggi, Luca; Bonci, Tecla; Del Din, Silvia; Micó-Amigo, M Encarna; Salis, Francesca; Bertuletti, Stefano; Caruso, Marco; Cereatti, Andrea; Gazit, Eran; Paraschiv-Ionescu, Anisoara; Soltani, Abolfazl; Kluge, Felix; Küderle, Arne; Ullrich, Martin; Kirk, Cameron; Hiden, Hugo; D'Ascanio, Ilaria; Hansen, Clint; Rochester, Lynn; Mazzà, Claudia; Chiari, Lorenzo.
Affiliation
  • Palmerini L; University of Bologna, Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', Bologna, Italy. luca.palmerini@unibo.it.
  • Reggi L; University of Bologna, Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy. luca.palmerini@unibo.it.
  • Bonci T; University of Bologna, Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy.
  • Del Din S; The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK.
  • Micó-Amigo ME; The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK.
  • Salis F; Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
  • Bertuletti S; Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
  • Caruso M; University of Sassari, Department of Biomedical Sciences, Sassari, Italy.
  • Cereatti A; University of Sassari, Department of Biomedical Sciences, Sassari, Italy.
  • Gazit E; Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy.
  • Paraschiv-Ionescu A; Politecnico di Torino, PolitoBIOMed Lab - Biomedical Engineering Lab, Torino, Italy.
  • Soltani A; Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy.
  • Kluge F; Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel.
  • Küderle A; Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
  • Ullrich M; Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
  • Kirk C; Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
  • Hiden H; Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
  • D'Ascanio I; Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
  • Hansen C; Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK.
  • Rochester L; Newcastle University, School of Computing, Newcastle, UK.
  • Mazzà C; University of Bologna, Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi', Bologna, Italy.
  • Chiari L; Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany.
Sci Data ; 10(1): 38, 2023 01 19.
Article in En | MEDLINE | ID: mdl-36658136
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.

Full text: 1 Database: MEDLINE Type of study: Clinical_trials / Guideline Language: En Year: 2023 Type: Article

Full text: 1 Database: MEDLINE Type of study: Clinical_trials / Guideline Language: En Year: 2023 Type: Article