EFI Moves: Individualized Diagnosis and Treatment in motion

FAU-eigene Forschungsfinanzierung: EFI / ETI / ...

Details zum Projekt

Prof. Dr. Jürgen Winkler
Prof. Dr. Jochen Klucken

Prof. Dr. Björn Eskofier
Prof. Dr. Friedrich Hennig
Prof. Dr. Dr. Matthias Lochmann
Prof. Dr. Klaus Pfeifer
Cristian Federico Pasluosta, Ph.D.
Dr. phil. Heiko Gaßner
Julius Hannink
Dr. med. Franz Marxreiter
Dr. med. Johannes Schlachetzki
Dr.-Ing. Felix Kluge
Nooshin Haji Ghassemi
Ivanna Timotius
Nils Roth
PD Dr. Simon Steib
Sarah Klamroth

Beteiligte FAU-Organisationseinheiten:
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Lehrstuhl für Sportbiologie und Bewegungsmedizin
Lehrstuhl für Sportwissenschaft mit dem Schwerpunkt Bewegung und Gesundheit
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Professur für Unfallchirurgie
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)

Akronym: EFIMoves
Projektstart: 01.01.2014
Projektende: 31.12.2016
Laufzeitverlängerung bis: 31.12.2017

Abstract (fachliche Beschreibung):

One of the most urgent needs for the public health care system is the missing objective and individualized efficacy assessment of medical diagnostics and intervention in prevention, early detection, and treatment of diseases. Present technology of sensor based motion analysis allows individualized and mobile assessment of mobility and activity-of-daily-living representing a very important readout for treatment efficacy for the most frequent diseases. The "diagnose related group" - DRG system in Germany compensates medical care solely based on diagnostic classification and medical procedures. Short- and long-term efficacy of any medical intervention is not included in the underlying reimbursement categories, thereby limiting the attribution of socioeconomic expenditure. Also, different sectors of health care (i.e. outpatient units, hospitals, etc.) further impede sustainable and objective assessment of the efficacy of medical diagnostics and therapeutic measures to improve quality-of-life.

The vision of our EFIMoves - EFI project is to combine modern and multimodal medical technological approaches for a diagnostic workup enabling a qualitative and quantitative assessment of impaired movement allowing a sustainable benchmarking of medical treatment. Mobile and integrated sensor based movement analysis provides a cost-effective, easy applicable, and individualized mobile assessment of any given movement. This sensor-based movement detection will be mirrored by modern high-tech diagnostic approaches using imaging modalities, MRI, biomechanical movement analysis.

This proposed concept has the potential to be applicable to any disorder affecting movement and mobility, however, as a proof-of-principle, the present project will focus on

- neuronal (e.g. Parkinson’s disease - PD) and

- musculoskeletal (e.g. Osteoarthritis - OA) related movement disorders.

Externe Partner

Adidas AG
Astrum IT GmbH
Fraunhofer-Institut für Integrierte Schaltungen (IIS)
Waldkrankenhaus St Marien
Geriatrie-in-Bayern Datenbank (GiB-DAT)
Ärztliche Arbeitsgemeinschaft zur Förderung der Geriatrie in Bayern (AFGiB e.V)
Medical Valley - Europäische Metropolregion Nürnberg
Peter Brehm GmbH
Siemens AG, Healthcare Sector

Go to first page Go to previous page 1 von 2 Go to next page Go to last page

Wanner, P., Schmautz, T., Kluge, F., Eskofier, B., Pfeifer, K., & Steib, S. (2019). Ankle angle variability during running in athletes with chronic ankle instability and copers. Gait & Posture, 68, 329–334.
Steib, S., Klamroth, S., Gaßner, H., Pasluosta, C.F., Eskofier, B., Winkler, J.,... Pfeifer, K. (2019). Exploring gait adaptations to perturbed and conventional treadmill training in Parkinson’s disease: Time-course, sustainability, and transfer. Human Movement Science. https://dx.doi.org/10.1016/j.humov.2019.01.007
Kluge, F., Hannink, J., Pasluosta, C.F., Klucken, J., Gaßner, H., Gelse, K.,... Krinner, S. (2018). Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty. Gait & Posture, 66, 194-200. https://dx.doi.org/10.1016/j.gaitpost.2018.08.026
Pasluosta, C.F., Steib, S., Klamroth, S., Gaßner, H., Goßler, J., Hannink, J.,... Eskofier, B. (2017). Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson’s disease after Perturbed Walking. Frontiers in Aging Neuroscience, 9, 316. https://dx.doi.org/10.3389/fnagi.2017.00316
Kluge, F., & Eskofier, B. (2017). Letter to the Editor regarding "Gait recording with inertial sensors - How to determine initial and terminal contact" by Bötzel and colleagues. Journal of Biomechanics, 52, 183-184. https://dx.doi.org/10.1016/j.jbiomech.2016.07.043
Steib, S., Klamroth, S., Gaßner, H., Pasluosta, C.F., Eskofier, B., Winkler, J.,... Pfeifer, K. (2017). Perturbation during treadmill training improves dynamic balance and gait in Parkinson’s disease: A single-blind randomized controlled pilot trial. Neurorehabilitation and Neural Repair, 31(8), 758-768. https://dx.doi.org/10.1177/1545968317721976
Klamroth, S., Steib, S., & Pfeifer, K. (2017). Sensomotorisches Laufbandtraining bei Morbus Parkinson. Zeitschrift für Komplementärmedizin, 5, 22-27. https://dx.doi.org/10.1055/s-0043-116646
Hannink, J., Kautz, T., Pasluosta, C.F., Gaßmann, K.-G., Klucken, J., & Eskofier, B. (2017). Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 21(1), 85--93. https://dx.doi.org/10.1109/JBHI.2016.2636456
Kluge, F., Krinner, S., Lochmann, M., & Eskofier, B. (2017). Speed dependent effects of laterally wedged insoles on gait biomechanics in healthy subjects. Gait & Posture, 55, 145-149. https://dx.doi.org/10.1016/j.gaitpost.2017.04.012
Golditz, T., Welsch, G., Pachowsky, M., Hennig, F., Pfeifer, K., & Steib, S. (2016). A multimodal approach to ankle instability: Interrelations between subjective and objective assessments of ankle status in athletes. Journal of Orthopaedic Research, 34(3), 525-532. https://dx.doi.org/10.1002/jor.23039

Zuletzt aktualisiert 2019-02-07 um 16:14