Janne M. Hahne
University of Göttingen
Publications 27
#1Carles Igual (Polytechnic University of Valencia)H-Index: 1
#2Jorge Igual (Polytechnic University of Valencia)H-Index: 10
Last.Lucas C. Parra (CCNY: City College of New York)H-Index: 51
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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...
1 CitationsSource
#1A. M. De Nunzio (GAU: University of Göttingen)H-Index: 1
#2Meike A. Schweisfurth (GAU: University of Göttingen)H-Index: 6
Last.Dario Farina (GAU: University of Göttingen)H-Index: 76
view all 14 authors...
1 CitationsSource
#1Strahinja DosenH-Index: 21
#2Gauravkumar K. Patel (GAU: University of Göttingen)H-Index: 3
Last.Dario Farina (Imperial College London)H-Index: 76
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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...
#1Sigrid S. G. Dupan (Radboud University Nijmegen)H-Index: 1
#2Ivan Vujaklija (Imperial College London)H-Index: 11
Last.Dario Farina (Imperial College London)H-Index: 76
view all 7 authors...
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...
#1Leonie Schmalfuss (GAU: University of Göttingen)H-Index: 2
#2Janne M. Hahne (GAU: University of Göttingen)H-Index: 9
Last.David Liebetanz (GAU: University of Göttingen)H-Index: 35
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2 CitationsSource
#1Gauravkumar K. Patel (GAU: University of Göttingen)H-Index: 3
#2Claudio Castellini (DLR: German Aerospace Center)H-Index: 26
Last.Strahinja Dosen (GAU: University of Göttingen)H-Index: 21
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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...
3 CitationsSource
#1Janne M. Hahne (GAU: University of Göttingen)H-Index: 9
#2Meike A. Schweisfurth (Hamburg University of Applied Sciences)H-Index: 6
Last.Dario Farina (Imperial College London)H-Index: 76
view all 4 authors...
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...
11 CitationsSource
#1Benjamin Paaßen (Bielefeld University)H-Index: 7
#2Alexander Schulz (Bielefeld University)H-Index: 38
Last.Barbara Hammer (Bielefeld University)H-Index: 33
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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...
3 CitationsSource
#1Janne M. HahneH-Index: 9
#2Marko MarkovicH-Index: 11
Last.Dario Farina (Imperial College London)H-Index: 76
view all 3 authors...
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...
31 CitationsSource
#1Han-Jeong HwangH-Index: 16
#2Janne M. HahneH-Index: 9
Last.Klaus-Robert MüllerH-Index: 92
view all 3 authors...
6 CitationsSource