REHYB: Rehabilitation based on Hybrid neuroprosthesis

The ReHyb project envisions a new cognitive rehabilitation system based on a digital user model obtained through a novel communication capability of multi-modal sensing and actuation techniques from exoskeleton and functional electrical stimulation (FES). The main advances of this system are the hybrid upper-body exoskeleton with improved wearability, cognitive abilities, and intrinsically safe low-level control scheme that autonomously analyses the user in a goal-oriented robot-assisted task.


Advancements in mechanical engineering and automation technologies have led to global expectations for robotic devices in rehabilitation to cope with a forecast of global ageing and shortage in clinical professionals in the near future. In particular, stroke patients often have to go through extensive rehabilitation or lose daily skills required for an independent self-determined life due to motor deficits. In contrast to classical physical therapists, robotic systems are able to tirelessly and precisely apply intense manual labour, while accurately measuring performance and improvements of the patient. Active exoskeletons meet these requirements and possess the additional advantage of non-stationary design that allows for flexible training and mobility of the patient. Preliminary studies indicate that the training efficiency can be improved if, in addition to the guidance by the exoskeleton, the users motor functions are actively controlled using functional electrical stimulation (FES). Such hybrid systems are advantageous because the users’ own muscular activity initialise the movements and are not passively guided through an external force. However, the required control which coordinates the active exoskeleton and stimulation for the human motor functions, especially in terms of dexterity skills necessary for activities of daily living, is more complex due to the unsolved questions on shared control and the missing models of the human motor function with respect to FES. Thus, the ReHyb project designs an upper-body hybrid neuroprosthesis using cooperative control strategies based on data-driven system identification and probabilistic estimation techniques for the internal human states, namely digital twin of a user. Our goal is a patient-specific, assist-as-needed device which maximises the training efficiency during home-based rehabilitation as means of serious gaming, and offers a pleasant user experience by supporting patients in daily life activities.

Horizon 2020 Research and Innovation Action 

Project title: ReHyb-Rehabilitation based on Neuroprosthesis
Project number: 871767
Call (part) identifier: H2020-ICT-2019-2
Project period: Jan 2020 - Dec 2023
Project website:

Research focus at TUM

TUM contributes its experience in control design for pHRI with a particular focus on human-adaptive control, control based on learned models, real-time system identification and active safety and accompanying psychophysical studies aiming at an improved and theoretically well founded collaboration of humans and robotic agents. Furthermore, TUM contributes to the design and implementation of complex HRI systems.

Relevant topics:


TUM team members

Sandra Hirche (principal investigator)

Satoshi Endo

Samuel Tesfazgi

Jan Brüdigam

Miruna Werkmeister (project manager)

Currently available student thesis

  • Learning for control of wrist-hand movements based on Functional Electric Stimulation (Samuel Tesfazgi) [PDF]
  • Higher-Order Variational Integrators for Robotic Systems in Maximal Coordinates (Jan Brüdigam) [PDF]
  • Trajectory Optimization and Control in Maximal Coordinates (Jan Brüdigam) [PDF]
  • Contact and Friction Dynamics for Robotic Systems in Maximal Coordinates (Jan Brüdigam) [PDF]
  • Neuromechanical modeling for physical Human-Exoskeleton-Interaction (Samuel Tesfazgi) [PDF]
  • Simulation of physical Human-Exoskeleton-Interaction with soft contacts (Samuel Tesfazgi) [PDF]

Selected publications

  • Johannsen, Leif; Potwar, Karna; Saveriano, Matteo; Endo, Satoshi; Lee, Dongheui: Robotic Light Touch Assists Human Balance Control During Maximum Forward Reaching. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2020, 001872082095053 more… BibTeX
  • Endo, Satoshi; Fröhner, Jakob; Music, Selma; Hirche, Sandra; Beckerle, Philipp: Effect of External Force on Agency in physical Human-Machine Interaction. Frontiers in Human Neuroscience 14, 2020 more… BibTeX
  • H. Börner; S. Endo; S. Hirche: Estimation of Involuntary Components of Human Arm Impedance in Multi-Joint Movements via Feedback Jerk Isolation. Frontiers in Neuroscience 14, 2020, 459 more… BibTeX
  • S. Music and S. Hirche: Haptic Shared Control for Human-Robot Collaboration: A Game-Theoretical Approach. Proceedings of the 21st IFAC World Congress, 2020 more… BibTeX
  • W. Xiao; A. Lederer; S. Hirche: Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression. Proceedings of the 21st IFAC World Congress , 2020 more… BibTeX
  • A. Lederer; A. Capone; S. Hirche: Parameter Optimization for Learning-based Control of Control-Affine Systems. Learning for Dynamics & Control, 2020 more… BibTeX