Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis

Du, Yi-Chun; Lin, Chia‐Hung; Shyu, Liang-Yu; Chen, Tainsong

doi:10.1016/j.eswa.2009.11.072

Cited by 108 publications

(55 citation statements)

References 16 publications

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“…Then, the pattern classification was employed. Neural network, fuzzy theory, probabilistic approach, support vector machine and grey relational analysis based classifier were adopted to achieve the recognition results [1,7,8].…”

Section: Introductionmentioning

confidence: 99%

Online Finger Gesture Recognition Using Surface Electromyography Signals

Li¹,

Li²

2013

JSIP

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The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.

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Section: Introductionmentioning

confidence: 99%

Online Finger Gesture Recognition Using Surface Electromyography Signals

Li¹,

Li²

2013

JSIP

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“…The number of EMG channels placed in a narrow uniform band/ring, which is proposed in the literature, is seven sensors (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002) and eight sensors (Andrews et al, 2009;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Saponas et al, 2008;Smith et al, 2009). When multiple sets of sensors were placed around the forearm, reasonable EMG signals with useful information could be acquired from the armband even the sensors are only approximately placed (Saponas et al, 2008).…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…one pair across the ulnar border (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002;Smith et al, 2008Smith et al, , 2009. In this scheme, however, there is no guarantee that electrodes are placed over the same muscles in all users.…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…(1) Lower forearm positions, i.e., around the wrist Tang, Liu, Lv, & Sun, 2012;You, Rhee, & Shin, 2010, 2011; (2) Upper forearm positions, i.e., below the elbow (Andews, Morin, & McLean, 2009;Benko et al, 2009;Du, Lin, Shyu, & Chen, 2010;Du, Shyu, & Hu, 2006;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Mogk & Keir, 2003;Saponas et al, 2008;Shyu, Chen, Tatn, & Hu, 2002;Smith, Huberdeau, Tenore, & Thakor, 2009;Smith et al, 2008;Tenore et al, 2007Tenore et al, , 2009). …”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

“…The landmark sensor armband is placed below the elbow in several conditions, e.g. approximately one third of the distance from the proximal end of a line from medial epicondyle to the distal head of the radius (Andrews et al, 2009;Mogk & Keir, 2003), or approximately two inches or five centimeters below the elbow (Du et al, 2006(Du et al, , 2010Shyu et al, 2002;Smith et al, 2009). For the landmark of the starting electrode pair, there are several ways, such as placing just superior to the ulna (Andrews et al, 2009) or over the flexor carpi radialis (Mogk & Keir, 2003).…”

Section: Emg Sensor Arm Bandsmentioning

confidence: 99%

See 2 more Smart Citations

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark

Quaine

Laurillau

Advances in Medical Technologies and Clinical Practice

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

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Driving Simulator Validation of Machine Learning Classification for a Surface Electromyography-Based Steering Assistance Interface

Nacpil

Nakano

2020

Advances in Simulation and Digital Human Modeling

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Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis

Cited by 108 publications

References 16 publications

Online Finger Gesture Recognition Using Surface Electromyography Signals

Online Finger Gesture Recognition Using Surface Electromyography Signals

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Driving Simulator Validation of Machine Learning Classification for a Surface Electromyography-Based Steering Assistance Interface

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