Development of a Neurally Controlled Hand-Exoskeleton for Functional Restoration Following Brain and Spinal Cord Injuries

  

Description:

Neural robotic control  by signals derived from the nervous system represents a promising approach to improving the autonomy and the quality of life of people with disabilities, like spinal cord injury and stroke patients. The aim of the KONSENS-NHE project is the development of a non-invasive, neurally controlled exoskeleton that may be used in everyday life to compensate for the loss of hand function in people with disabilities. For this purpose, EEG-based brain-machine interface technology is integrated with advanced machine learning methods and novel mechatronic solutions for the realization of a hand exoskeleton. The project is a collaboration between the University and University Clinic of Tübingen, the University of Stuttgart, Reutlingen University, and the Fraunhofer Institute for Process Automation (IPA) in Stuttgart. It is funded by the Baden-Württemberg Stiftung as part of the neurorobotics framework. Our group’s role in the project is to characterize finger-hand-arm coordination in both healthy individuals and in clinical patients using data from different sensors, including motion capture, inertial motion sensors, and normal and high-density EMG. We use machine learning techniques to identify appropriate control signals and to model the sensorimotor processes underlying arm/hand coordination during complex goal-oriented reaching tasks. Our current investigations focus on employing Dynamic Movement Primitives (DMPs) and Gaussian Process Dynamical Models (GPDMs) for generating real-time movement. Developed models will be ultimately embedded in the control architecture of the exoskeleton in collaboration with the other project partners.

Publications

Taubert, N., St-Amand, J., Kumar, P., Gizzi, L. & Giese, M. A. (2020). Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(127-140). [More]