In biomechanics, we create details models of human kinematic and dynamic properties of arms, hands, fingers, and legs. These models are needed to understand which properties of human movement are intrinsic---caused by muscles, tendons, ligaments and bones---and which are controlled by the nervous system. Our resulting models are used in the construction and control of novel robotic systems, including prosthetic hands and robotic arms and legs.

The use of surface electromyography (sEMG) for prosthetic control has been in place since the 1960's. We go a step further. On the one hand, we optimise the conditioning of the sEMG signal, and find new ways of relating it to limb movement. But we also look at different channels to control prosthetic and assistive robotic devices, including central nervous system implants.

In Machine Learning, we investigate methods to map high-dimensional non-linear data within a control process. Even though most of our data are related to the above fields of research, the methods we employ and develop are general methods, in which we combine deep belief networks with time sequence learning.

Limb rehabilitation and prosthetics are paramount applications of the techniques developed in biomimetic robotics. We focus upon human-computer interfaces to aid the disabled regain the lost limb functionality. In our view, both rehabilitation and prosthetics rely on re-establishing the sensori-motor loop with the missing limb. This includes both ways: feed-forward control by detecting the patient’s will to move and sensorial feedback by transducing digital readings to feelings.