FPGA-based acceleration of high density myoelectric signal processing

A. Boschmann, A. Agne, L.M. Witschen, G. Thombansen, F. Kraus, M. Platzner, in: 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2016.

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Conference Paper | Published | English
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Abstract
In recent years, advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis that is capable of performing training and classification of an amputee's EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. Using the Xilinx Zynq as a low-cost off-the-shelf reconfigurable processing platform, we present a solution that is able to compute prosthesis control signals from multi-channel EMG input with up to 256 channels with a maximum processing delay of less than a single millisecond. While the presented system is able to perform training as well as classification, most of our efforts were focused on the acceleration of the feature extraction units, achieving a speed-up of 6.7 for feature extraction alone, and 4.8 for the total processing time as compared to a software only solution.
Publishing Year
Proceedings Title
2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)
Conference
2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)
Conference Location
Mexico City, Mexico
Conference Date
2015-12-07 – 2015-12-09
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Boschmann A, Agne A, Witschen LM, Thombansen G, Kraus F, Platzner M. FPGA-based acceleration of high density myoelectric signal processing. In: 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). IEEE; 2016. doi:10.1109/reconfig.2015.7393312
Boschmann, A., Agne, A., Witschen, L. M., Thombansen, G., Kraus, F., & Platzner, M. (2016). FPGA-based acceleration of high density myoelectric signal processing. In 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). Mexico City, Mexico: IEEE. https://doi.org/10.1109/reconfig.2015.7393312
@inproceedings{Boschmann_Agne_Witschen_Thombansen_Kraus_Platzner_2016, title={FPGA-based acceleration of high density myoelectric signal processing}, DOI={10.1109/reconfig.2015.7393312}, booktitle={2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)}, publisher={IEEE}, author={Boschmann, Alexander and Agne, Andreas and Witschen, Linus Matthias and Thombansen, Georg and Kraus, Florian and Platzner, Marco}, year={2016} }
Boschmann, Alexander, Andreas Agne, Linus Matthias Witschen, Georg Thombansen, Florian Kraus, and Marco Platzner. “FPGA-Based Acceleration of High Density Myoelectric Signal Processing.” In 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). IEEE, 2016. https://doi.org/10.1109/reconfig.2015.7393312.
A. Boschmann, A. Agne, L. M. Witschen, G. Thombansen, F. Kraus, and M. Platzner, “FPGA-based acceleration of high density myoelectric signal processing,” in 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), Mexico City, Mexico, 2016.
Boschmann, Alexander, et al. “FPGA-Based Acceleration of High Density Myoelectric Signal Processing.” 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2016, doi:10.1109/reconfig.2015.7393312.

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