Project leader: Björn Eskofier, Felix Kluge, Jochen Klucken, Heiko Gaßner

Project members: Felix Kluge, Arne Küderle, Martin Ullrich

Start date: 1. April 2019

End date: 31. March 2024

Funding source: Europäische Union (EU)

Abstract

Optimal treatment of the impaired mobility resulting from ageing and chronic disease is one of the 21st century's greatest challenges facing patients, society, governments, healthcare services, and science. New interventions are a key focus. However, to accelerate their development, we need better ways to detect and measure mobility loss. Digital technology, including body worn sensors, has the potential to revolutionise mobility assessment. The overarching objectives of MOBILISE-D are threefold: to deliver a valid solution (consisting of sensor, algorithms, data analytics, outcomes) for real-world digital mobility assessment; to validate digital outcomes in predicting clinical outcome in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture recovery and congestive heart failure; and, to obtain key regulatory and health stakeholder approval for digital mobility assessment. The objectives address the call directly by linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D consists of 35 partners from 13 countries with long, successful collaboration, combining the requisite expertise to address the technical and clinical challenges. To achieve the objectives, partners will jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes can successfully predict relevant clinical outcomes and provide a better, safer and quicker way to arrive at the development of innovative medicines. MOBILISE-D's results will directly facilitate drug development, and establish the roadmap for clinical implementation of new, complementary tools to identify, stratify and monitor disability, so enabling widespread, cost-effective access to optimal clinical mobility management through personalised healthcare.

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 820820. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

www.mobilise-d.eu

Related Publications

• Ullrich M., Küderle A., Hannink J., Del Din S., Gaßner H., Marxreiter F., Klucken J., Eskofier B., Kluge F.:
  Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies
  In: IEEE Journal of Biomedical and Health Informatics 24 (2020), p. 1869 - 1878
  ISSN: 2168-2194
  DOI: 10.1109/JBHI.2020.2975361
  URL: https://www.mad.tf.fau.de/files/2020/11/2020_ullrich_gaitsequencedetection.pdf
  BibTeX: Download
• Polhemus AM., Bergquist R., Bosch de Basea M., Brittain G., Buttery SC., Chynkiamis N., Dalla Costa G., Delgado Ortiz L., Demeyer H., Emmert K., Garcia Aymerich J., Gaßner H., Hansen C., Hopkinson N., Klucken J., Kluge F., Koch S., Leocani L., Maetzler W., Micó-Amigo ME., Mikolaizak AS., Piraino P., Salis F., Schlenstedt C., Schwickert L., Scott K., Sharrack B., Taraldsen K., Troosters T., Vereijken B., Vogiatzis I., Yarnall A., Mazza C., Becker C., Rochester L., Puhan MA., Frei A.:
  Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review
  In: BMJ Open 10 (2020), p. e038704-
  ISSN: 2044-6055
  DOI: 10.1136/bmjopen-2020-038704
  BibTeX: Download
• Kluge F., Del Din S., Cereatti A., Gaßner H., Hansen C., Helbostad JL., Klucken J., Küderle A., Mueller A., Rochester L., Ullrich M., Eskofier B., Mazza C.:
  Consensus based framework for digital mobility monitoring
  In: PLoS ONE 16 (2021)
  ISSN: 1932-6203
  DOI: 10.1371/journal.pone.0256541
  BibTeX: Download
• Mazza C., Alcock L., Aminian K., Becker C., Bertuletti S., Bonci T., Brown P., Brozgol M., Buckley E., Carsin AE., Caruso M., Caulfield B., Cereatti A., Chiari L., Chynkiamis N., Ciravegna F., Del Din S., Eskofier B., Evers J., Aymerich JG., Gazit E., Hansen C., Hausdorff JM., Helbostad JL., Hiden H., Hume E., Paraschiv-Ionescu A., Ireson N., Keogh A., Kirk C., Kluge F., Koch S., Küderle A., Lanfranchi V., Maetzler W., Mico-Amigo ME., Mueller A., Neatrour I., Niessen M., Palmerini L., Pluimgraaff L., Reggi L., Salis F., Schwickert L., Scott K., Sharrack B., Sillen H., Singleton D., Soltani A., Taraldsen K., Ullrich M., Van Gelder L., Vereijken B., Vogiatzis I., Warmerdam E., Yarnall A., Rochester L.:
  Technical validation of real-world monitoring of gait: a multicentric observational study
  In: BMJ Open 11 (2021)
  ISSN: 2044-6055
  DOI: 10.1136/bmjopen-2021-050785
  BibTeX: Download
• Ullrich M., Küderle A., Reggi L., Cereatti A., Eskofier B., Kluge F.:
  Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data
  2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (, 1. November 2021 - 5. November 2021)
  DOI: 10.1109/EMBC46164.2021.9630653
  BibTeX: Download