EMG Measurement with Textile-Based Electrodes in Different Electrode Sizes and Clothing Pressures for Smart Clothing Design Optimization

Kim, Siyeon; Lee, So Jung; Jeong, Wonyoung

doi:10.3390/polym12102406

Cited by 52 publications

(57 citation statements)

References 29 publications

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“…The motion artifact is counterbalanced by the big size of textile electrodes which target the activity of a whole muscle group, contrary to traditional electrodes that capture the activity of single muscular units [ 45 ]. Textile-based electrodes used in a study denoted comparable performance to Ag/AgCl electrodes when the diameter of the electrode was greater than 20 mm and the applied pressure greater than 10 mmHg, while the baseline noise showed a tendency to increase with decreasing electrode size [ 53 ].…”

Section: Measurement Of Physiological Parametersmentioning

confidence: 99%

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Angelucci

Cavicchioli

Cintorrino

et al. 2021

Sensors

105

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Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as healthcare, fitness, and work. This can be achieved with the application of textile technologies for sensorized garments, where the sensors are completely embedded in the fabric. The complete integration of sensors in the fabric leads to several manufacturing techniques that allow dealing with both the technological challenges entailed by the physiological parameters under investigation, and the basic requirements of a garment such as perspiration, washability, and comfort. This review is intended to provide a detailed description of the textile technologies in terms of materials and manufacturing processes employed in the production of sensorized fabrics. The focus is pointed at the technical challenges and the advanced solutions introduced with respect to conventional sensors for recording different physiological parameters, and some interesting textile implementations for the acquisition of biopotentials, respiratory parameters, temperature and sweat are proposed. In the last section, an overview of the main garments on the market is depicted, also exploring some relevant projects under development.

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Section: Measurement Of Physiological Parametersmentioning

confidence: 99%

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Angelucci

Cavicchioli

Cintorrino

et al. 2021

Sensors

105

View full text Add to dashboard Cite

show abstract

“…The most common measurement technology used for these purposes is surface electromyography (sEMG) [6,73,100,115,784,841], that involves placing electrodes on top of the skin, either as individual single sensors or embedded onto a wearable smart compressive garment suit [189,286,411,752,896]. Kim et al [411] studied the effect of garment t, i.e. clothing pressure, to sEMG signal delity, and showed higher pressure led to improved signal-to-noise ratio (SNR) highlighting the need for customisation of smart garment.…”

Section: Muscle Activation Measurementmentioning

confidence: 99%

“….31236/osf.io/w734a ditionally in contrast to cameras, the IMUs can be worn by the athletes as a smart compressive garment[411,893], developed for example by companies such as Xsens TM (https://www.xsens.com/), Cape Bionics TM (https://www.capebionics.com/) and Teslasuit TM https://teslasuit.io/. The wearable compressive garment is relatively invisible to the athlete, but the technology has not gained the market traction as hoped by the companies and practitioners [155].…”

mentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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“…In addition, e-textile sEMG electrode size and locations have been investigated. Kim, Lee and Jeong found out that signal-to-noise ratio of EMG signals increased significantly as the e-textile electrode, fabricated from silver and carbon conductive sheet, diameter increased [10]. On the other hand, as the e-textile diameter increased, the trade-off was the increase of the cross talk between the e-textile electrodes.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Sleeve Pattern and Fit on E-Textile Electromyography (EMG) Electrode Performance in Smart Clothing Design

Goncu-Berk

Tuna

2021

Sensors

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When e-textile EMG electrodes are integrated into clothing, the fit of the clothing on the body, and therefore its pattern and cut become important factors affecting the EMG signal quality in relation to the seamless contact between the skin and the e-textile electrode. The research so far on these effects was conducted on commercially available clothing or in tubular sleeve forms for arms. There is no study that investigated different clothing pattern and fit conditions and their effect on e-textile EMG electrode performance. This study investigates the effect of clothing pattern and fit in EMG applications using e-textile electrodes integrated onto the sleeves of custom drafted t-shirts in set-in and raglan sleeve pattern variations. E-textile electrode resistance, signal-to-noise ratio (SNRdB), power spectral density and electrode–skin impedance are measured and evaluated in set-in sleeve and raglan sleeve conditions with participants during a standardized arm movement protocol in comparison to the conventional hydrogel Ag/AgCl electrodes. The raglan sleeve pattern, widely used in athletic wear to provide extra ease for the movement of the shoulder joint, showed superior performance and therefore indicated the pattern and cut of a garment could have significant effect on EMG signal quality in designing smart clothing.

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EMG Measurement with Textile-Based Electrodes in Different Electrode Sizes and Clothing Pressures for Smart Clothing Design Optimization

Cited by 52 publications

References 29 publications

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

The Effect of Sleeve Pattern and Fit on E-Textile Electromyography (EMG) Electrode Performance in Smart Clothing Design

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