Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors

Li, Yun; Chen, Xiang; Tian, Jianxun; Xu, Z.; Wang, Kongqiao; Yang, Jian

doi:10.1145/1891903.1891926

Cited by 56 publications

(63 citation statements)

References 16 publications

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“…The most popular choices of cla ssifiers in rec ent work are based on marginal advantages in classification performance; these include linear discriminant analysis (LDA) [33], support vector machines [34][35][36][37], and hidden Markov models [38][39]. The main advantage of LDA is its simplicity of implementation (especially in an embedded processor) and ease of training.…”

Section: Best Practices In Emg Pattern Recognitionmentioning

confidence: 99%

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme

Englehart²

2011

JRRD

818

613

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Abstract-Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable op tion. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

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Section: Best Practices In Emg Pattern Recognitionmentioning

confidence: 99%

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme

Englehart²

2011

JRRD

818

613

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show abstract

“…The combination of EMG and accelerometers has previously been used by Roy et al [14] for monitoring patients with stroke and by Li et al [15] for sign language detection and game control. To the best of our knowledge the combination of EMG and accelerometers has not been used in conjunction with prosthesis control.…”

mentioning

confidence: 99%

Resolving the Limb Position Effect in Myoelectric Pattern Recognition

Fougner

Scheme

Chan

et al. 2011

IEEE Trans. Neural Syst. Rehabil. Eng.

330

290

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Abstract--Reported studies on pattern recognition of electromyograms (EMG) for the control of prosthetic devices traditionally focus on classification accuracy of signals recorded in a laboratory. The difference between the constrained nature in which such data are often collected and the unpredictable nature of prosthetic use is an example of the semantic gap between research findings and a viable clinical implementation. In this work, we demonstrate that the variations in limb position associated with normal use can have a substantial impact on the robustness of EMG pattern recognition, as illustrated by an increase in average classification error from 3.8% to 18%. We propose to solve this problem by (1) collecting EMG data and training the classifier in multiple limb positions and by (2) measuring the limb position with accelerometers. Applying these two methods to data from ten normally limbed subjects, we reduce the average classification error from 18% to 5.7% and 5.0%, respectively. Our study shows how sensor fusion (using EMG and accelerometers) may be an efficient method to mitigate the effect of limb position and improve classification accuracy.

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“…Additionally, they are unable to handle non-manual gestures [4]. EMG (Electromyography) sensor measures electrical signals generated by muscle cells and is used by some works [4], [5]. The technique however suffers from random noises.…”

Section: Sign Language Recognitionmentioning

confidence: 99%

Towards establishing a mute communication: An Indian Sign Language perspective

Kane

Khanna

2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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To automate the communication between a deaf and a hearing person, both channels i.e. from deaf to hearing and vice-versa must be emphasized equally. Sign Language Recognition System helps to understand the sign language gestures made by a deaf, while a Sign Language Animation System enables a deaf person to interpret a hearing person who does not know sign language. In this survey, various techniques to implement these two systems are explored and compared separately. Though Indian Sign language (ISL) is in focus, research work in other sign languages is explored as well.Index Terms-Indian Sign Language (ISL), sign language recognition, sign language animation, signing avatar.

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Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors

Cited by 56 publications

References 16 publications

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Resolving the Limb Position Effect in Myoelectric Pattern Recognition

Towards establishing a mute communication: An Indian Sign Language perspective

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