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

Room: Room 01.012

Group Members


Current Students

  • Lena Janousek
    Machine learning based improvement of the diagnostic accuracy of an online symptom checker
  • Annika Mücke
    Detection of semi-standardized gait tests from free-living inertial sensor recordings in Parkinson’s disease
  • Anne-Marie Strauch
    Identification of signal features leading to reduced validity of IMU-based gait (variability) parameters
  • Hannah Willms
    Improving and validating existing methods for IMU based stride segmentation based on dynamic time warping


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

Running Projects