SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

E. Ceseracciu; M. Reggiani; Z. Sawacha; M. Sartori; F. Spolaor; C. Cobelli; E. Pagello

doi:10.1109/ROMAN.2010.5598664

SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

E. Ceseracciu^*, M. Reggiani, Z. Sawacha, M. Sartori, F. Spolaor, C. Cobelli, E. Pagello

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

30 Citations (Scopus)

Abstract

The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phasedependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMGbased exoskeleton for rehabilitation purposes.

Original language	English
Title of host publication	19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010
Pages	165-170
Number of pages	6
DOIs	https://doi.org/10.1109/ROMAN.2010.5598664
Publication status	Published - 13 Dec 2010
Externally published	Yes
Event	19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010 - Viareggio, Italy Duration: 12 Sept 2010 → 15 Sept 2010 Conference number: 19

Conference

Conference	19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010
Abbreviated title	RO-MAN 2010
Country/Territory	Italy
City	Viareggio
Period	12/09/10 → 15/09/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ROMAN.2010.5598664

Cite this

Ceseracciu, E., Reggiani, M., Sawacha, Z., Sartori, M., Spolaor, F., Cobelli, C., & Pagello, E. (2010). SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics. In 19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010 (pp. 165-170). Article 5598664 https://doi.org/10.1109/ROMAN.2010.5598664

@inproceedings{0c7e3b2c27d84f269b3c34753b1c81dc,

title = "SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics",

abstract = "The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phasedependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMGbased exoskeleton for rehabilitation purposes.",

author = "E. Ceseracciu and M. Reggiani and Z. Sawacha and M. Sartori and F. Spolaor and C. Cobelli and E. Pagello",

year = "2010",

month = dec,

day = "13",

doi = "10.1109/ROMAN.2010.5598664",

language = "English",

isbn = "9781424479917",

pages = "165--170",

booktitle = "19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010",

note = "19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010, RO-MAN 2010 ; Conference date: 12-09-2010 Through 15-09-2010",

}

Ceseracciu, E, Reggiani, M, Sawacha, Z, Sartori, M, Spolaor, F, Cobelli, C & Pagello, E 2010, SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics. in 19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010., 5598664, pp. 165-170, 19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010, Viareggio, Italy, 12/09/10. https://doi.org/10.1109/ROMAN.2010.5598664

SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics. / Ceseracciu, E.; Reggiani, M.; Sawacha, Z. et al.
19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010. 2010. p. 165-170 5598664.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

AU - Ceseracciu, E.

AU - Reggiani, M.

AU - Sawacha, Z.

AU - Sartori, M.

AU - Spolaor, F.

AU - Cobelli, C.

AU - Pagello, E.

N1 - Conference code: 19

PY - 2010/12/13

Y1 - 2010/12/13

N2 - The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phasedependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMGbased exoskeleton for rehabilitation purposes.

AB - The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A phasedependent approach, based on foot contact and foot push off events, was employed in order to contextualize muscle activation signals. Good accuracy is demonstrated on experimental data from three healthy subjects. Classification has also been tested for different subsets of EMG features and muscles, aiming to identify a minimal setup required for the control of an EMGbased exoskeleton for rehabilitation purposes.

UR - http://www.scopus.com/inward/record.url?scp=78649873423&partnerID=8YFLogxK

U2 - 10.1109/ROMAN.2010.5598664

DO - 10.1109/ROMAN.2010.5598664

M3 - Conference contribution

AN - SCOPUS:78649873423

SN - 9781424479917

SP - 165

EP - 170

BT - 19th International Symposium in Robot and Human Interactive Communication, RO-MAN 2010

T2 - 19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010

Y2 - 12 September 2010 through 15 September 2010

ER -

SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

Abstract

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this