Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Alex Marchioni; Mauro Mangia; Fabio Pareschil; Riccardo Rovatti; Gianluca Setti

doi:10.1109/BIOCAS.2018.8584763

Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Alex Marchioni, Mauro Mangia, Fabio Pareschil, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.

Original language	English (US)
Title of host publication	2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538636039
DOIs	https://doi.org/10.1109/BIOCAS.2018.8584763
State	Published - Dec 20 2018
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-02-15

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10.1109/BIOCAS.2018.8584763

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Marchioni, A., Mangia, M., Pareschil, F., Rovatti, R., & Setti, G. (2018). Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2018.8584763

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title = "Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain",

abstract = "Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.",

author = "Alex Marchioni and Mauro Mangia and Fabio Pareschil and Riccardo Rovatti and Gianluca Setti",

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Marchioni, A, Mangia, M, Pareschil, F, Rovatti, R & Setti, G 2018, Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain. in 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BIOCAS.2018.8584763

Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain. / Marchioni, Alex; Mangia, Mauro; Pareschil, Fabio et al.
2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

AU - Marchioni, Alex

AU - Mangia, Mauro

AU - Pareschil, Fabio

AU - Rovatti, Riccardo

AU - Setti, Gianluca

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

PY - 2018/12/20

Y1 - 2018/12/20

N2 - Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.

AB - Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.

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UR - http://www.scopus.com/inward/record.url?scp=85060865479&partnerID=8YFLogxK

U2 - 10.1109/BIOCAS.2018.8584763

DO - 10.1109/BIOCAS.2018.8584763

M3 - Conference contribution

SN - 9781538636039

BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Abstract

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