Digital Health – Gait Analytics
Our group focuses on the development and application of novel hardware and software tools for movement and gait assessment. To optimally assess functional limitations in real-world conditions, we develop unobtrusive wearable sensor systems that can acquire movement and other physiological data in the clinic as well as in the patients’ home environment.
The obtained clinical data in general and home monitoring data in particular are sensitive with regard to privacy and require new storage, handling, and access concepts. As a basis for safe and efficient data processing we investigate appropriate data management concepts and contribute to novel medical data infrastructures.
From an analytical perspective, the efficient data processing of those large data sets requires sophisticated signal processing and machine learning tools to efficiently derive clinically relevant parameters, such as interpretable spatio-temporal parameters. This allows us to gain insight into disease related symptoms and mechanisms in order to provide feedback to patients, researchers and physicians regarding clinical interventions, treatment efficacy, or disease progression. Artificial intelligence tools are used to create decision support systems that may support clinicians to optimize and individualize treatments. By developing novel movement analysis algorithms based on signal processing and machine learning and by improving existing state-of-the-art methods we aim to shape a healthy digital future.
Group Head
Dr.-Ing. Felix Kluge
- Address:
Room 01.012 - Phone number: +49 9131 85 27921
- Email: felix.kluge@fau.de
Group Members
Current Students
-
Martin Freudensprung
Observer-independent scratching and wiping detection for pruritus and pain assessment in mice (Bachelor's Thesis) -
Matthias Tietsch
Development and Validation of algorithms for estimating steps and gait speed from inertial sensors worn around the waist (Master’s Thesis) -
Pia Gierse
Objective Assessment of Chorea in Huntington´s Disease using Wearable Motion Sensors (Master's Thesis)
If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.
Completed Projects
- BayMED Mobile GaITLab
- EFI Moves
- Information management system for automated quality assessment in radiotherapy
- MotionLab@Home
- MoveIT
- Smart annotation using semi-supervised methods
- BayMED Sleep intervention analysis
- HOOP – platform for rhythmic stimulation, home based Parkinson’s patient rehabilitation
- Gait Analysis in Rodents
