Left/Right Contributions of Articulatory Muscles in Speech Recognition Using High-Density Surface Electromyography

2019 ◽  
Author(s):  
Xiaochen Wang ◽  
Mingxing Zhu ◽  
Jiashuo Zhuang ◽  
Dan Wang ◽  
Shixiong Chen ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Surface Electromyography ◽  
High Density ◽  
Density Surface

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

2021 ◽  
Vol 95 ◽  
pp. 103456
Author(s):  
Tiwana Varrecchia ◽  
Alberto Ranavolo ◽  
Silvia Conforto ◽  
Alessandro Marco De Nunzio ◽  
Michail Arvanitidis ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Frequency Dependent ◽  
Density Surface

scholarly journals Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Application in Neurorobotics

2021 ◽  
Author(s):  
Tianyun Sun ◽  
Qin Hu ◽  
Jacqueline Libby ◽  
S. Farokh Atashzar
Keyword(s):  
Surface Electromyography ◽  
Complex Model ◽  
High Density ◽  
Transient Phase ◽  
Spatiotemporal Resolution ◽  
Semg Signal ◽  
Training Time ◽  
Motor Intention ◽  
Density Surface ◽  
Human Robot Interfaces

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of dilation foci to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

FC7.2 Using high-density surface electromyography (HD-sEMG) in detecting neuromuscular disorders in children

2006 ◽  
Vol 117 ◽  
pp. 1-2 ◽  
Author(s):  
J.P. van Dijk ◽  
D. Kusters ◽  
N. van Alfen ◽  
M.J. Zwarts ◽  
D.F. Stegeman ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Neuromuscular Disorders ◽  
High Density ◽  
Density Surface

Identifying Motor Units in Longitudinal Studies with High-Density Surface Electromyography

2016 ◽  
pp. 147-151
Author(s):  
Eduardo Martinez-Valdes ◽  
Francesco Negro ◽  
Christopher M. Laine ◽  
Deborah L. Falla ◽  
Frank Mayer ◽  
...  
Keyword(s):  
Longitudinal Studies ◽  
Surface Electromyography ◽  
Motor Units ◽  
High Density ◽  
Density Surface

scholarly journals Transcutaneous innervation zone imaging from high-density surface electromyography recordings

2020 ◽  
Vol 17 (1) ◽  
pp. 016070
Author(s):  
Yang Liu ◽  
Chuan Zhang ◽  
Nicholas Dias ◽  
Yen-Ting Chen ◽  
Sheng Li ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
High Density ◽  
Innervation Zone ◽  
Density Surface

High‐density surface electromyography: A visualization method of laryngeal muscle activity

2019 ◽  
Vol 129 (10) ◽  
pp. 2347-2353 ◽  
Author(s):  
David J. Bracken ◽  
Gladys Ornelas ◽  
Todd P. Coleman ◽  
Philip A. Weissbrod
Keyword(s):  
Muscle Activity ◽  
Surface Electromyography ◽  
High Density ◽  
Visualization Method ◽  
Laryngeal Muscle ◽  
Density Surface
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