Intuitive Interaction with Body-Worn Technology

Measurements are performed on humans in the motion laboratory.

The ability to provide intuitive, safe and adapted support to the user is one of the key functionalities of any physical assistance system. This relies on the correct interpretation of motion and biological signals (like EMG and ECG) to recognize accurately user's intentions and needs for support. For this purpose, pattern recognition algorithms as well as machine learning and sensor fusion approaches must be used. One ongoing research topic in this area is the development of the support-as-needed control strategies for industrial exoskeletons.

Admittance and impedance control approaches based on models of the human-machine system are used for the safe control of the supporting forces, e.g. in order to smoothly follow and support the user’s motions or to insure a stable behavior when using serial-elastic actuators. Numerical software and rapid prototyping systems from renowned manufacturers accelerate controller design and ensure high reliability through intensive test runs. The designed controlled can then be implemented in hardware and optimized using human-in-the-loop experiments.

Our offers

We can support you in the following development steps of active physical assistance systems:

  • Conceptual design of the control strategies and required sensor system
  • Development of control algorithms using rapid prototyping systems
  • Design of algorithms for the detection and interpretation of motion or biological signals
  • Design and realization of electronic and software architecture
  • Human-in-the-loop optimization

Reference projects

 

BMBF Project – ExoHaptik

Development of a haptic output device in the form of an exoskeleton, which can provide caregivers with a realistic feeling of strain and thus be used as a training device.

 

BMBF Project – ExoPflege

A user-friendly, actively powered and anthropomorphic exoskeleton demonstrator will be developed, optimized and evaluated for high-stress care settings.