Muscle analysis of hand and forearm during tapping using surface electromyography

Masayuki Yokoyama; Ryohei Koyama; Masao Yanagisawa

doi:10.1109/GCCE.2015.7398505

Muscle analysis of hand and forearm during tapping using surface electromyography

Masayuki Yokoyama, Ryohei Koyama, Masao Yanagisawa

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

2 Citations (Scopus)

Abstract

Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.

Original language	English
Title of host publication	2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	595-598
Number of pages	4
ISBN (Print)	9781479987511
DOIs	https://doi.org/10.1109/GCCE.2015.7398505
Publication status	Published - 2016 Feb 3
Event	4th IEEE Global Conference on Consumer Electronics, GCCE 2015 - Osaka, Japan Duration: 2015 Oct 27 → 2015 Oct 30

Other

Other	4th IEEE Global Conference on Consumer Electronics, GCCE 2015
Country/Territory	Japan
City	Osaka
Period	15/10/27 → 15/10/30

Keywords

dorsal interossei
EMG
finger
flexor digitorum superficialis
flexor muscle
lumbrical
signal-to-noise ratio
tapping

ASJC Scopus subject areas

Instrumentation
Biotechnology
Computer Networks and Communications
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/GCCE.2015.7398505

Cite this

Muscle analysis of hand and forearm during tapping using surface electromyography. / Yokoyama, Masayuki; Koyama, Ryohei; Yanagisawa, Masao.
2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 595-598 7398505.

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

Yokoyama, M, Koyama, R & Yanagisawa, M 2016, Muscle analysis of hand and forearm during tapping using surface electromyography. in 2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015., 7398505, Institute of Electrical and Electronics Engineers Inc., pp. 595-598, 4th IEEE Global Conference on Consumer Electronics, GCCE 2015, Osaka, Japan, 15/10/27. https://doi.org/10.1109/GCCE.2015.7398505

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title = "Muscle analysis of hand and forearm during tapping using surface electromyography",

abstract = "Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.",

keywords = "dorsal interossei, EMG, finger, flexor digitorum superficialis, flexor muscle, lumbrical, signal-to-noise ratio, tapping",

author = "Masayuki Yokoyama and Ryohei Koyama and Masao Yanagisawa",

year = "2016",

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doi = "10.1109/GCCE.2015.7398505",

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booktitle = "2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015",

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AU - Koyama, Ryohei

AU - Yanagisawa, Masao

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Y1 - 2016/2/3

N2 - Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.

AB - Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.

KW - dorsal interossei

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KW - finger

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KW - flexor muscle

KW - lumbrical

KW - signal-to-noise ratio

KW - tapping

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ER -

Muscle analysis of hand and forearm during tapping using surface electromyography

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Keywords

ASJC Scopus subject areas

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