Gait analysis in geriatric patients using mobile sensor systems and machine learning algorithms

Third party funded individual grant

Project Details

Project leader:
Prof. Dr. Björn Eskofier
Prof. Dr. Jochen Klucken

Project members:
Malte Ollenschläger

Contributing FAU Organisations:
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Molekular-Neurologische Abteilung in der Neurologischen Klinik

Funding source: Industrie (AGAPLESION gAG)
Start date: 15/01/2018
End date: 15/01/2021

Research Fields

Machine Learning and Data Analytics
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)

Abstract (technical / expert description):

Walking is a key element of human mobility and independence. For persons aged 70 or above, the number of falls per year increases drastically. Physiological consequences are bone fractures, traumas or death. In conjunction with psychological consequences, such as post-fall anxiety, falls lead to a decreased quality of life. Most falls could be prevented if an early detection of fall risk was available, thus maintaining a high quality of life.

This project will focus on assessing gait in geriatric patients using sensor-based gait analysis. Inertial sensors will be used to measure risk-of-fall related gait parameters for geriatric patients at hospitals of AGAPLESION gAG (AGAPLESION DIAKONIEKLINIKUM HAMBURG and AGAPLESION MARKUS KRANKENHAUS in Frankfurt am Main). The acquired data will be processed at the Machine Learning and Data Analytics Lab of FAU. The machine-learning algorithms that will be developed, will help to improve diagnostics and to measure therapeutic success.


External Partners


Last updated on 2019-28-02 at 11:19