Evolutionary optimization of user intent recognition for transfemoral amputees

Gholamreza Khademi; Hanieh Mohammadi; Dan Simon; Elizabeth C. Hardin

doi:10.1109/BioCAS.2015.7348280

Evolutionary optimization of user intent recognition for transfemoral amputees

Gholamreza Khademi
, Hanieh Mohammadi
, Dan Simon
, Elizabeth C. Hardin

Electrical and Computer Engineering

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

23 Scopus citations

Abstract

Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96%.

Original language	English
Title of host publication	IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479972333
DOIs	https://doi.org/10.1109/BioCAS.2015.7348280
State	Published - Dec 4 2015
Event	11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States Duration: Oct 22 2015 → Oct 24 2015

Conference

Conference	11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Country/Territory	United States
Period	10/22/15 → 10/24/15

Keywords

evolutionary algorithm
K nearest neighbor
lower-limb prosthesis
user intent recognition

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10.1109/BioCAS.2015.7348280

Cite this

Khademi, G., Mohammadi, H., Simon, D., & Hardin, E. C. (2015). Evolutionary optimization of user intent recognition for transfemoral amputees. In IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings Article 7348280 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS.2015.7348280

@inproceedings{806e8cf03e984dec8b4be45d5ac98106,

title = "Evolutionary optimization of user intent recognition for transfemoral amputees",

abstract = "Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96\%.",

keywords = "evolutionary algorithm, K nearest neighbor, lower-limb prosthesis, user intent recognition",

author = "Gholamreza Khademi and Hanieh Mohammadi and Dan Simon and Hardin, \{Elizabeth C.\}",

year = "2015",

month = dec,

day = "4",

doi = "10.1109/BioCAS.2015.7348280",

language = "English",

booktitle = "IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 ; Conference date: 22-10-2015 Through 24-10-2015",

}

Khademi, G, Mohammadi, H, Simon, D & Hardin, EC 2015, Evolutionary optimization of user intent recognition for transfemoral amputees. in IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings., 7348280, Institute of Electrical and Electronics Engineers Inc., usa, 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015, United States, 10/22/15. https://doi.org/10.1109/BioCAS.2015.7348280

Evolutionary optimization of user intent recognition for transfemoral amputees. / Khademi, Gholamreza; Mohammadi, Hanieh; Simon, Dan et al.
IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings. usa: Institute of Electrical and Electronics Engineers Inc., 2015. 7348280.

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

TY - GEN

T1 - Evolutionary optimization of user intent recognition for transfemoral amputees

AU - Khademi, Gholamreza

AU - Mohammadi, Hanieh

AU - Simon, Dan

AU - Hardin, Elizabeth C.

PY - 2015/12/4

Y1 - 2015/12/4

N2 - Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96%.

AB - Lower-limb prosthetic legs help amputees regain their walking ability. User intent recognition is utilized to infer human gait mode (fast walk, slow walk, etc.) so the controller can be adjusted depending on the detected gait mode. In this paper, mechanical sensor data is collected from an able-bodied subject and used for user intent recognition. Feature extraction, principal component analysis, correlation analysis, and K-nearest neighbor methods are used, modified, and optimized with an evolutionary algorithm for improved performance. The optimized system successfully classifies four different walking modes with an accuracy of 96%.

KW - evolutionary algorithm

KW - K nearest neighbor

KW - lower-limb prosthesis

KW - user intent recognition

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U2 - 10.1109/BioCAS.2015.7348280

DO - 10.1109/BioCAS.2015.7348280

M3 - Conference contribution

BT - IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

CY - usa

T2 - 11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015

Y2 - 22 October 2015 through 24 October 2015

ER -

Evolutionary optimization of user intent recognition for transfemoral amputees

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Keywords

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