FallRiskPD

Project leader: Jürgen Winkler, Jochen Klucken, Björn Eskofier

Project members: Till Gladow, Heiko Gaßner, Julius Hannink, Martin Ullrich, Nils Roth

Start date: 1. January 2018

End date: 31. December 2019

Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)

In cooperation with: UK Erlangen – Department for molecular neurology

Abstract

The ability to walk defines human nature and limits the mobility, independence, and quality of life. Falls are the leading cause of both fatal and nonfatal injuries among older adults, causing severe injuries such as hip fractures, head trauma, and death. Increased fall risk is a key symptom in Parkinson’s disease (PD), limiting the independency and mobility of patients.

So far, no validated technical solutions exist to identify the individual’s rising fall risk before the first fall occurs. Therefore, we will investigate algorithms, that are able to predict the fall risk based on specific gait patterns, captured by shoe integrated inertial sensors. The data for the evaluation of fall risk associated gait patterns will be acquired by means of a continuous long-term monitoring system.

To ensure a successful progress of this project we will combine three strategies in the research and development phase:

1. Usage of distinct sensors that enable gait assessment with high biomechanical resolution
2. Development and evaluation of machine learning based gait pattern algorithms
3. Digital biobanking of clinical distinct gait patterns to individualize fall risk monitoring.

The overall goal of the project is the investigation of novel machine learning based algorithms that enable the determination of PD patients’ fall risk using continuous gait data. Since existing algorithms and test procedures in related clinical research are typically limited to one-time assessments, we will investigate new algorithms for a continuous gait monitoring system, that will identify disease specific changes of gait with a high reliability.

At the same time, we will generate clinical understanding and validated data for individualized applicability that is required for medical product licensing, as well as economic effect sizes of technology application in healthcare strategies.