Medical Informatics Initiative Junior Research Group
"Integration and Analysis of Multimodal Sensor Signals and Clinical Data for Diagnostics and Investigation of Neurological Movement Disorders" (MoveGroup)

As part of the HIGHmed consortium, the junior research group, which is headed by Dr. Sebastian Fudickar and funded within the Medical Informatics Initiative framework, investigates wearable and ambient diagnostic systems for the combined assessment of motor, cognitive and sensory abilities. The differentiated monitoring of these abilities are essential in assessing the functionality of elderly people to identify causalities between cognitive and motor deficits and thus enable specific, resource-oriented therapy approaches.

For this purpose, wearable and ambient sensor technologies and classification and fusion algorithms will be prototypically investigated and evaluated for the diagnostics of functional abilities and might benefit an improved understanding of normal aging or abnormal individual progressions. Based on these deliverables, personalized interventions with used interaction will be designed to enhance motor and cognitive performance.

The junior research group develops, implements and evaluates new methods for the integration and analysis of multimodal sensor signals and clinical data for the diagnosis and investigation of movement disorders. Thereby, the scientific objectives and the research work of the project are structured along the following three main objectives:

Objective 1 - Sensor-based acquisition, modeling of body movements:
By building a multimodal sensor platform for detailed acquisition of body movements and developing an algorithmic processing chain for sensor data fusion and feature extraction, a precise, quantitative analysis of body movements will be enabled.

Objective 2 - HiGHmed-compliant data integration and usability:
To integrate and exploit relevant sensor-based movement models and profiles for care and research processes, a data warehouse will be developed in compliance with data protection and ethical regulations and implications and linked to the UKSH MeDIC medical data integration center. 

Objective 3 - Decision support and knowledge gain with AI methods:
For the development of an AI-based decision support for the medical care of patients with movement disorders, machine learning models will be developed based on the collected multimodal movement data.

Project details

Thesis supervision

With over 60 supervised theses, we are pleased to regularly supervise final theses (Bachelor, Master), internships and similar formats. If you are interested in one of the listed exemplary topics, please book an appointment for an online discussion directly here. We will then present the topics to you in more detail and, in mutual consultation, work out possible assignments tailored to your needs. Of course, we will also adapt the degree of difficulty of the work to the type of degree you are aiming for. 

Current topics:

We are looking forward to meeting you!