Research Cluster 10

Human-adaptive automation systems


This research cluster and the Rehabilitation Robotics research group at Fraunhofer IPK are supervised by Prof Dr.-Ing. Joerg Krueger. The cluster does research on multimodal robotic devices for motor rehabilitation after stroke.

Currently supervised scholarship holders:

Approaches in supporting motor learning in device-assisted neurorehabilitation

Dipl.-Psych. Dipl.-Wirt.-Inf.(FH) Simone Schmid

1st supervisor: Prof. Dr.-Ing. Joerg Krueger
2nd supervisor: Prof. Dr. Manfred Thuerig

Description: In this dissertation, two approaches are implemented and investigated, which should improve treatment intensity in device-assisted motor rehabilitation after stroke. First, effort and perseverance of stroke patients will be increased by a goal setting intervention. In the second part, cues extracted from a kinematic motion analysis will be used as feedback to improve movement quality and re-direct attention to the therapy session. .

Motor Learning through Multimodal Human-Robot-Interaction

Dipl.-Psych. Luara Ferreira dos Santos

1st supervisor: Prof. Dr.-Ing. Jörg Krüger
2nd supervisor: Prof. Dr. Manfred Thüring 

Description: Motor re-learning of voluntary movements is the central goal of neurological rehabilitation in patients after stroke. Multimodal robotic training devices are a key element of modern rehabilitation therapy. The objective of this research project is to design a prospective and improved human-technology-interaction during rehabilitation treatment. The focus is on the analysis and modeling of motor learning process of the patient and their physical interaction with multimodal robotic training devices.

Haptic Interaction and Training Algorithms in Stroke Rehabilitation

Dipl.-Ing. Robert Steingraeber

1st supervisor: Prof. Dr.-Ing. Joerg Krueger
2nd supervisor: Prof. Dr.-Ing. Marc Kraft

Description: This project established a research environment for arm rehabilitation of stroke patients. It facilitates the comparison of device learning algorithms, methods for patient assessment and teaching algorithms via a telehaptic connection. The goal is an optimal force support by a rehabilitation device. That means a reduction of both position error and support energy. Simulations and first case studies compare three device learning strategies and indicate a better adaptation with variable learning rate, which should be reformulated in the frequency domain to obtain the required robustness. Modes for the measurements of muscle, reflex and learning properties are included as well, to improve the understanding of the patient and consequently the support algorithms. Finally, a telehaptic connection between to devices was established, enabling investigations of the teaching by a therapist.

Supervised student projects

Current student projects

Finished student projects

Christian Nitz
Torque control and design extension for a rehabilitation device (master thesis)
Dipl.-Ing. Robert Steingraeber | FSP 10

Mandy Skubich, Christian Sulzberger, Nadine Poetter
Developing training games and evaluation algorithms for robot-assisted motoric neurorehabilitation after stroke using the example of ?Reha Slides? (student project)
Dipl.-Psych., Dipl.-Wirt.-Inf. (FH) Simone Schmid, M.Eng. Sylvia Donner | FSP 10, FSP 1

Dana Willfroth
Alternativ concept of operations in motoric rehabilitation after stroke using the example of a low-cost headtracking system (bachelor thesis)
Dipl.-Psych., Dipl.-Wirt.-Inf. (FH) Simone Schmid, Dipl.-Ing. Robert Steingraeber | FSP 10

Mareike Okrafka
Model based torque estimation for a motor test rig (bachelor thesis)
Dipl.-Ing. Robert Steingraeber | FSP 10

Felix Sinell
Auswertung von Therapiedaten zur Verbesserung der automatisierten motorischen Armrehabilitation (Bachelorarbeit)
Dipl.-Ing. Robert Steingräber | FSP 10