Parkinson’s disease is a rapidly spreading and the second most common neurodegenerative disorder after Alzheimer’s dementia. The increasing prevalence worldwide resembles many features typically observed during a pandemic, except for an infectious cause. Although much progress to advance the field has been made through conventional clinical studies, the technical advancement brought up new methods. Our goal is to use Machine Learning to explore new approaches in the treatment as well as in the diagnosis of Parkinson’s.

In our research project ParkProReakt, we are improving the quality of life of Parkinson's patients by monitoring the course of the disease with the help of wearables. Doctors rarely see their patients, so their diagnoses are based on a snapshot of the patient's current condition. In ParkProReakt, we collect sensor data from Parkinson's patients that is processed using machine learning algorithms, providing doctors with a continuous picture of changes in the course of the disease and the state of their patients' health.

In collaboration with Prof. Christine Klein (Institute of Neurogenetics, University of Lübeck), we are developing a platform on which patients can determine the subtype of their Parkinson's disease. Since the best treatment and the expected course of the disease is strongly depend on the subtype, it is crucial for Parkinson's patients to identify their subtype as early as possible. Our machine learning algorithms predict the Parkinson's subtype of patients based on demographic information and existing symptoms.

Contact person

Dr.-Ing. Xinyu Huang

Third party funded projects

G-BA Project: ParkProReakt - Proactive rather than reactive symptom recognition in Parkinson's disease patients. Duration: 01.01.2022 - 31.12.2024.

Website

https://parkpro.parkinson-marburg.de/