Abstract Myoelectric Control with an Arduino-based System

Hancong Wu; Sigrid Dupan; Kianoush Nazarpour

Abstract Myoelectric Control with an Arduino-based System

Hancong Wu, Sigrid Dupan, Kianoush Nazarpour

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

Abstract / Description of output

This paper presents the design and evaluation of an Arduino-based system for electromyogram (EMG) signal measurement and prosthesis control with the abstract decoder. It achieves a 2 kHz sampling rate for two EMG channels, processes EMG signals on-the-fly and sends the prosthesis control command via a CAN bus. We tested the accuracy and responsiveness of the system in real-time by playing back previously recorded EMG signals through a Tip, Ring, and Sleeve (TRS) function generator. The correlation coefficients between the mean absolute value (MAV) of the original signals and the measured signals were above 97%.

Original language	English
Title of host publication	MEC20 Symposium Proceedings
Pages	167-170
Number of pages	4
Publication status	Published - 23 Jul 2020
Event	Myoelectric Controls Symposium 2020 - Fredericton, Canada Duration: 10 Aug 2020 → 13 Aug 2020 https://www.unb.ca/research/institutes/biomedical/mec/

Symposium

Symposium	Myoelectric Controls Symposium 2020
Abbreviated title	MEC 2020
Country/Territory	Canada
City	Fredericton
Period	10/08/20 → 13/08/20
Internet address	https://www.unb.ca/research/institutes/biomedical/mec/

Access to Document

https://conferences.lib.unb.ca/index.php/mec/article/view/71Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

Sensorimotor learning for control of prosthetic limbs
Nazarpour, K.
1/02/18 → 31/01/23
Project: Research

Cite this

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title = "Abstract Myoelectric Control with an Arduino-based System",

abstract = "This paper presents the design and evaluation of an Arduino-based system for electromyogram (EMG) signal measurement and prosthesis control with the abstract decoder. It achieves a 2 kHz sampling rate for two EMG channels, processes EMG signals on-the-fly and sends the prosthesis control command via a CAN bus. We tested the accuracy and responsiveness of the system in real-time by playing back previously recorded EMG signals through a Tip, Ring, and Sleeve (TRS) function generator. The correlation coefficients between the mean absolute value (MAV) of the original signals and the measured signals were above 97%.",

author = "Hancong Wu and Sigrid Dupan and Kianoush Nazarpour",

year = "2020",

month = jul,

day = "23",

language = "English",

pages = "167--170",

booktitle = "MEC20 Symposium Proceedings",

note = "Myoelectric Controls Symposium 2020, MEC 2020 ; Conference date: 10-08-2020 Through 13-08-2020",

url = "https://www.unb.ca/research/institutes/biomedical/mec/",

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T1 - Abstract Myoelectric Control with an Arduino-based System

AU - Wu, Hancong

AU - Dupan, Sigrid

AU - Nazarpour, Kianoush

PY - 2020/7/23

Y1 - 2020/7/23

N2 - This paper presents the design and evaluation of an Arduino-based system for electromyogram (EMG) signal measurement and prosthesis control with the abstract decoder. It achieves a 2 kHz sampling rate for two EMG channels, processes EMG signals on-the-fly and sends the prosthesis control command via a CAN bus. We tested the accuracy and responsiveness of the system in real-time by playing back previously recorded EMG signals through a Tip, Ring, and Sleeve (TRS) function generator. The correlation coefficients between the mean absolute value (MAV) of the original signals and the measured signals were above 97%.

AB - This paper presents the design and evaluation of an Arduino-based system for electromyogram (EMG) signal measurement and prosthesis control with the abstract decoder. It achieves a 2 kHz sampling rate for two EMG channels, processes EMG signals on-the-fly and sends the prosthesis control command via a CAN bus. We tested the accuracy and responsiveness of the system in real-time by playing back previously recorded EMG signals through a Tip, Ring, and Sleeve (TRS) function generator. The correlation coefficients between the mean absolute value (MAV) of the original signals and the measured signals were above 97%.

M3 - Conference contribution

SP - 167

EP - 170

BT - MEC20 Symposium Proceedings

T2 - Myoelectric Controls Symposium 2020

Y2 - 10 August 2020 through 13 August 2020

ER -

Abstract Myoelectric Control with an Arduino-based System

Abstract / Description of output

Symposium

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Projects

Sensorimotor learning for control of prosthetic limbs

Cite this