Physiotherapy Assistance
The KIBA Lab represents an exemplary sensor-environment designed to bolster diagnostics, therapy, and aftercare through the integration of AI-assisted robotics.
Our primary focus is in the field of patient-specific movement diagnostics and therapy, with a keen emphasis on gait analysis. We are collaborating with the Institut for Electrical Engineering (IME), the Institut of the Health Science (IfG) and Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE).
Within our state-of-the-art laboratory, we've meticulously assembled the latest equipment in gait analysis technology, including the cutting-edge M-Gait treadmill system. This innovative device is seamlessly integrated with Vicon cameras, harnessing the combined power of video-based, IMU, and force-pressure sensors.
Additionally, the Float system is a groundbreaking component of the KIBA project, offering advanced gait analysis capabilities. It operates as a dynamic multidirectional overhead body weight support (BWS) system, providing crucial assistance to patients with gait impairments during their training and rehabilitation efforts to restore natural locomotion. This remarkable technology aids individuals recovering from conditions such as stroke, spinal cord or brain injuries, incomplete paraplegia, or orthopedic issues, allowing them to regain the ability to walk and engage in safe, three-dimensional training.
Within the advanced KIBA Lab, a plethora of collaborative research projects involving our medical informatics researchers in the Move Group and physiotherapists are underway. These projects encompass diverse areas such as gait event detection and perturbation simulations. This robust collaboration is poised to yield new opportunities for further cooperation and the publication of groundbreaking findings.
In the previous project SenseVojta, a sensor-based system to support diagnostics, therapy and aftercare according to the Vojta principle, that makes elementary
movement patterns accessible in people with damaged central nervous system and locomotor system was developed and evaluated in cooperation with the Social Pediatric Center of the DRK Pediatric Clinic Siegen. For this purpose we design an electronic inertial measurement unit (IMU), which is equipped with several sensors.
We use four IMUs that are placed along the arm to record the subtle stimulations.
The integrated sensors record some medically relevant parameters of the patient as well as the movement parameters in 3D space.
Contact person
Prof. Dr.-Ing. Marcin Grzegorzek
Dr. rer. nat. habil. Sebastian Fudickar
Third party funded projects and publications
BMBF-Project: SenseVojta - Sensor-based Diagnosis, Therapy and Aftercare According to the Vojta Principle. Duration: 01.12.2016 - 30.11.2019.
Khan, M.H.; Schneider, M.; Farid, M.S.; Grzegorzek, M. Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model. Sensors 2018, 18, 3202.
- Research
- AI und Deep Learning in Medicine
- Medical Image Processing and VR-Simulation
- Integration and Utilisation of Medical Data
- Sensor Data Analysis for Assistive Health Technologies
- Medical Image Computing and Artificial Intelligence
- Medical Data Science Lab
- Medical Deep Learning Lab
- Medical Data Engineering Lab
- Junior Research Group Diagnostics and Research of Movement Disorders
Contact person
Sebastian Fudickar
Head of Junior Research Group
Gebäude MFC8,2OG
,
Raum 16
sebastian.fudickar(at)uni-luebeck.de
+49 451 3101 5640