Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

Sung, Jung‐Joon; Baek, Seol-Hee; Park, Jin‐Woo; Rho, Jeong Hwa; Kim, Byung Jo

doi:10.3390/s23125490

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…Most users prefer the latter method. The raw signal from EMG sensors must be processed using various techniques, including pattern recognition methods [4].…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

Risandrya,

Sinaga,

Pamungkas

2024

Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau Islands, Indone

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Electromyography (EMG) Signals from human muscles can be utilized in various fields. One of them is in the control field. Some control systems use a person's finger movements. So, an algorithm is needed to recognize hand movement patterns. This paper examines systems' capabilities using neural network algorithms with discrete wavelet transforms. The signal is obtained from the EMG signal generated by the surface EMG sensor. This sensor is issued on the user's upper arm. This study used three healthy subjects with five-finger movements. This system is able to recognize patterns of finger movements, about 79.79%.

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“…Most users prefer the latter method. The raw signal from EMG sensors must be processed using various techniques, including pattern recognition methods [4].…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

Risandrya,

Sinaga,

Pamungkas

2024

Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau Islands, Indone

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A patterned mechanical–electrical coupled sensing patch for multimodal muscle function evaluation

Xue,

Zou,

Wan

et al. 2024

InfoMat

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Muscles, the fundamental components supporting all human movement, exhibit various signals upon contraction, including mechanical signals indicating tremors or mechanical deformation and electrical signals responsive to muscle fiber activation. For noninvasive wearable devices, these signals can be measured using surface electromyography (sEMG) and force myography (FMG) techniques, respectively. However, relying on a single source of information is insufficient for a comprehensive evaluation of muscle condition. In order to accurately and effectively evaluate the various states of muscles, it is necessary to integrate sEMG and FMG in a spatiotemporally synchronized manner. This study presents a flexible sensor for multimodal muscle state monitoring, integrating serpentine‐structured sEMG electrodes with fingerprint‐like FMG sensors into a patch approximately 250 μm thick. This design achieves a multimodal assessment of muscle conditions while maintaining a compact form factor. A thermo‐responsive adhesive hydrogel is incorporated to enhance skin adhesion, improving the signal‐to‐noise ratio of the sEMG signals (33.07 dB) and ensuring the stability of the FMG sensor during mechanical deformation and tremors. The patterned coupled sensing patch demonstrates its utility in tracking muscular strength, assessing fatigue levels, and discerning features of muscle dysfunction by analyzing the time‐domain and frequency‐domain characteristics of the mechanical–electrical coupled signals, highlighting its potential application in sports training and rehabilitation monitoring.image

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Improving Motion Intention Recognition for Trans-Radial Amputees Based on sEMG and Transfer Learning

Lin,

Niu,

Zhang

et al. 2023

Applied Sciences

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Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG) signals obtained from the remaining forearm muscles of trans-radial amputees. This technology sheds new light on myoelectric prosthesis control; however, fewer signals from amputees can be collected in clinical practice. The collected signals can further suffer from quality deterioration due to the muscular atrophy of amputees, which significantly decreases the accuracy of hand motion intention recognition. To overcome these problems, this work proposed a transfer learning strategy combined with a long-exposure-CNN (LECNN) model to improve the amputees’ hand motion intention recognition accuracy. Transfer learning can leverage the knowledge acquired from intact-limb subjects to amputees, and LECNN can effectively capture the information in the sEMG signals. Two datasets with 20 intact-limb and 11 amputated-limb subjects from the Ninapro database were used to develop and evaluate the proposed method. The experimental results demonstrated that the proposed transfer learning strategy significantly improved the recognition performance (78.1%±19.9%, p-value < 0.005) compared with the non-transfer case (73.4%±20.8%). When the source and target data matched well, the after-transfer accuracy could be improved by up to 8.5%. Compared with state-of-the-art methods in two previous studies, the average accuracy was improved by 11.6% (from 67.5% to 78.1%, p-value < 0.005) and 12.1% (from 67.0% to 78.1%, p-value < 0.005). This result is also among the best from the contrast methods.

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Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

Cited by 4 publications

References 52 publications

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

A patterned mechanical–electrical coupled sensing patch for multimodal muscle function evaluation

Improving Motion Intention Recognition for Trans-Radial Amputees Based on sEMG and Transfer Learning

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