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Janne M. Hahne
University of Göttingen
27Publications
7H-index
273Citations
Publications 27
Newest
Carles Igual (Polytechnic University of Valencia), Jorge Igual9
Estimated H-index: 9
(Polytechnic University of Valencia)
+ 1 AuthorsLucas C. Parra46
Estimated H-index: 46
(CCNY: City College of New York)
In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a 2-D arena. We tested this on 15 able-bodied and three limb-deficient participants using an eight-channel myoelectric armband. The human–machine pairs lear...
Published on Dec 1, 2018in Journal of Neural Engineering4.55
A.M. De Nunzio1
Estimated H-index: 1
(GAU: University of Göttingen),
Meike A. Schweisfurth5
Estimated H-index: 5
(GAU: University of Göttingen)
+ 11 AuthorsT Weiss (FSU: University of Jena)
Published on Oct 16, 2018
Strahinja Dosen17
Estimated H-index: 17
,
Gauravkumar K. Patel2
Estimated H-index: 2
(GAU: University of Göttingen)
+ 2 AuthorsDario Farina70
Estimated H-index: 70
(Imperial College London)
The contemporary myoelectric prostheses are advanced mechatronic systems, but human-machine interfacing for robust control of these devices is still an open challenge. We present a novel method for the recognition of user intention based on pattern classification which is inspired by the natural coordination of multiple muscles during hand and wrist motions. The coordinated muscle activation produces a characteristic distribution of the amplitude features of the electromyography signals, and the...
Published on Oct 16, 2018
Sigrid S. G. Dupan (Radboud University Nijmegen), Ivan Vujaklija9
Estimated H-index: 9
(Imperial College London)
+ 4 AuthorsDario Farina70
Estimated H-index: 70
(Imperial College London)
State-of-the-art prosthetic hands allow separate control of all digits. Restoring natural hand use with these systems requires simultaneous and proportional control of all fingers. Regression algorithms might be able to predict any combination of degrees of freedom after training them separately. However, to the best of our knowledge, this has yet to be shown online. Twelve able-bodied participants were instructed to reach predefined target forces representing either single or combined finger pr...
Published on Oct 1, 2018in Journal of Neural Engineering4.55
Leonie Schmalfuss1
Estimated H-index: 1
(GAU: University of Göttingen),
Leonie Schmalfuss1
Estimated H-index: 1
(GAU: University of Göttingen)
+ 6 AuthorsDavid Liebetanz35
Estimated H-index: 35
(GAU: University of Göttingen)
Gauravkumar K. Patel2
Estimated H-index: 2
(GAU: University of Göttingen),
Claudio Castellini26
Estimated H-index: 26
(DLR: German Aerospace Center)
+ 2 AuthorsStrahinja Dosen17
Estimated H-index: 17
(GAU: University of Göttingen)
Dexterous upper limb myoelectric prostheses can, to some extent, restore the motor functions lost after an amputation. However, ensuring the reliability of myoelectric control is still an open challenge. In this paper, we propose a classification method that exploits the regularity in muscle activation patterns (uniform scaling) across different force levels within a given movement class. This assumption leads to a simple training procedure, using training data collected at single contraction in...
Published on Jun 20, 2018
Janne M. Hahne7
Estimated H-index: 7
(GAU: University of Göttingen),
Meike A. Schweisfurth5
Estimated H-index: 5
(Hamburg University of Applied Sciences)
+ 1 AuthorsDario Farina70
Estimated H-index: 70
(Imperial College London)
Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world cond...
Published on Feb 1, 2018in Neurocomputing4.07
Benjamin Paaßen6
Estimated H-index: 6
(Bielefeld University),
Alexander Schulz36
Estimated H-index: 36
(Bielefeld University)
+ 1 AuthorsBarbara Hammer31
Estimated H-index: 31
(Bielefeld University)
Abstract Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance, leading for example to misclassifications of data. This is particularly apparent in the domain of bionic hand prostheses, where machine learning models promise faster and more intuitive user interfaces, but are hindered by their lack of robustness to everyday disturbances, such as electrode shifts. One way t...
Published on Dec 1, 2017in Scientific Reports4.01
Janne M. Hahne7
Estimated H-index: 7
,
Marko Markovic10
Estimated H-index: 10
,
Dario Farina70
Estimated H-index: 70
(Imperial College London)
State of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer a significant increase in functionality, but their clinical impact is limited, mainly due to lack of reliability. In this study, we analyse two conceptually different machine learning approaches, focusing on their robustness and performance in a closed loop application. A classification (fi...
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