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.



Measuring intrinsic arm stiffness has been addressed in research the last 30 years but is still a big challenge that has not been solved yet. What has been measured so far is either reflex-affected or so-called lumped stiffness, combining inertial, damping and stiffness effects. The idea behind measuring intrinsic stiffness is to do a proper in-vivo analysis of the biomechanical system and to identify stiffness w.r.t. muscle activation for continuous stiffness measurements during movement. 

In a collaboration with the startup company simplias GmbH, we are looking for a junior software developer who will work on the software "mobile field report" within the ASP.NET framework. Simplias' goal is to develop software for mobile working, and you can play a central role in this development.

We have built a new measurement setup for measuring human leg impedance, consisting of two linear motors and two force-torque sensors to perturb the human legs while standing on it. By also measuring muscular activity using surface EMG, we can find answers as to how human stabilise at different perturbation frequencies and which muscles influence this stance how. Initial proof-of-concept measurements have to be extended by setting up a model of the perturbed system, measuring joint positions and velocities, and so on. Measuring the position and the force plus EMG will allow us to analyse the behaviour of leg impedance within the spinal circuitry feedback loops.

You will develop a force feedback device to give a sense of touch back to hand prosthetic patients. The homunculus shows comparable sensitivity areas for the fingers and toes: essentially, toes have a coarsely comparable representation in the brain as fingers when it comes to skin sensitivity. Consequently, they seem to be ideal candidates to replace finger touch sensing for hand amputees.

Picture of  Sebastián Aced

Sebastián Aced

DLR: student
EMG hardware
sebstian.aceddlrde, +49 8153 28-1056
Picture of  Justin Bayer

Justin Bayer

TUM: PhD candidate
time series learning
bayer.justingooglemailcom
Picture of  Daniele Casaburo

Daniele Casaburo

TUM: student
EMG source separation
daniele.casaburotumde
Picture of  Claudio Castellini

Claudio Castellini

DLR: postdoc
prosthetics and rehabilitation
claudio.castellinidlrde, +49 8153 28-1093
Picture of  Constantin Böhm

Constantin Böhm

DLR: student
arm impedance
constantin.boehmdlrde, +49 8153 28-1056
Picture of  Nadine Fligge

Nadine Fligge

DLR: PhD candidate
human grasping
nadine.fliggedlrde
Picture of  Dominikus Gierlach

Dominikus Gierlach

DLR: student
neural impedance control
dominikus.gierlachdlrde, +49 8153 28-1056
Picture of  Andreas Goß

Andreas Goß

DLR: student
rigid-body human finger model
a.gossdlrde
Picture of  Agneta Gustus

Agneta Gustus

DLR: PhD candidate
human hand dynamics
Picture of  Barbara Hilsenbeck

Barbara Hilsenbeck

DLR: student
EMG finger mapping
barbara.hilsenbeckdlrde
Picture of  Hannes Hoeppner

Hannes Hoeppner

DLR: PhD candidate
 human arm impedance
hannes.hoeppnerdlrde, +49 8153 28-1062
Picture of  Rachel Hornung

Rachel Hornung

DLR: student (with medical robotics group)
novelty detection with the Mica robot
rachel.hornungdlrde
Picture of  Daniela Korhammer

Daniela Korhammer

DLR: Student
EEG/EMG
korhammdin.tumde
Picture of  Dominic Lakatos

Dominic Lakatos

DLR: PhD candidate
human arm dynamics
dominic.lakatosdlrde, +49 8153 28-2467
Picture of  Marvin Ludersdorfer

Marvin Ludersdorfer

DLR: student
human arm impedance
marvin.ludersdorferdlrde
Picture of  Christian Osendorfer

Christian Osendorfer

TUM: PhD candidate
unsupervised learning, deep networks
osendorfin.tumde
Picture of  Thomas Rückstiess

Thomas Rückstiess

TUM: PhD candidate
reinforcement learning and design
rueckstiin.tumde
Picture of  David Sierra González

David Sierra González

DLR: Student
ultrasound hand movement
david.sierragonzalezdlrde, +49 8153 28-1056
Picture of  Patrick van der Smagt

Patrick van der Smagt

DLR,TUM: Director of BRML labs
smagtdlrde, +49 8153 281152
Picture of  Georg Stillfried

Georg Stillfried

DLR: PhD candidate
kinematics of the human hand
georg.stillfrieddlrde
Picture of  Michael Strohmayr

Michael Strohmayr

DLR: PhD candidate
the DLR artificial skin
michael.strohmayrdlrde
Picture of  Sebastian Urban

Sebastian Urban

TUM: PhD candidate
learning skin data
surbantumde, +49 89 289-25787
Picture of  Holger Urbanek

Holger Urbanek

DLR: PhD candidate
EMG conditioning
holger.urbanekdlrde, +49 8153 28-2450
Picture of  Jörn Vogel

Jörn Vogel

DLR: PhD candidate
BCI robot control
joern.vogeldlrde, +49 8153 28-2166
Picture of  Stefan Zoell

Stefan Zoell

DLR: student
research design
stefan.zoelldlrde



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Below our 15 most recent publications. If you need more, follow the link. And note: All downloadable PDFs are for personal use only. Please do not redistribute.

Synek A, Stillfried G (2012). Multi-body simulation of a human thumb joint by sliding surfaces. IEEE International Conference on Biomedical Robotics and Biomechatronics
Atzori M, Gijsberts A, Heynen S, Mittaz-Hager A, Deriaz O, Smagt P van der, Castellini C, Caputo B, Müller H (2012). Building the NINAPRO Database: A Resource for the Biorobotics Community. IEEE International Conference on Biomedical Robotics and Biomechatronics
Gierlach D, Gustus A, Smagt P van der (2012). Generating marker stars for 6D optical tracking. IEEE International Conference on Biomedical Robotics and Biomechatronics
Cordella F, Corato FD, Zollo L, Siciliano B, Smagt P van der (2012). Patient performace evaluation using kinect and Monte Carlo-based finger tracking. IEEE International Conference on Biomedical Robotics and Biomechatronics
Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, Smagt P van der, Donoghue JP (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 485 372-377.
Fligge N, McIntyre J, Smagt P van der (2012). Minimum jerk for human catching movements in 3D. Proc. IEEE International Conference on Biomedical Robotics and Biomechatronics
Rückstieß T, Osendorfer C, Smagt P van der (2012). Minimizing Data Consumption with Sequential Online Feature Selection. International Journal of Machine Learning and Cybernetics.
Bayer J, Osendorfer C, Smagt P van der (2011). Learning sequence neighbourhood metrics. NIPS 2011 Workshop Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity
Castellini C, Passig G (2011). Ultrasound image features of the wrist are linearly related to finger positions. Proc. IROS---International Conference on Intelligent Robots and Systems
Castellini C, Smagt P van der (2011). Preliminary evidence of dynamic muscular synergies in human grasping. Proceedings of ICAR - International Conference on Advanced Robotics
Höppner H, Lakatos D, Urbanek H, Castellini C, Smagt P van der (2011). The Grasp Perturbator: Calibrating human grasp stiffness during a graded force task. Proc. ICRA---International Conference on Robotics and Automation 3312-3316 .
Lakatos D (2011). Identifikation der Impedanzparameter des menschlichen Arms mit dem sieben-Achs DLR Leichtbauroboter. Master thesis: Hochschule für angewandte Wissenschaften München
Lakatos D, Petit F, Smagt P van der (2011). Conditioning vs. Excitation Time for Estimating Impedance Parameters of the Human Arm. Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots
Osendorfer C, Schlüter J, Schmidhuber J, Smagt P van der (2011). Unsupervised learning of low-level audio features for music similarity estimation. Workshop on Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing, ICML 2011
Perretta R (2011). A preliminary study in EMG-based Upper Limb Stroke Rehabilitation. Master thesis: UNIVERSITÀ DEGLI STUDI DI NAPOLI FEDERICO II