Review on electromyography signal acquisition and processing

Gohel, Vidhi; Mehendale, Ninad

doi:10.1007/s12551-020-00770-w

Cited by 52 publications

(29 citation statements)

References 36 publications

(30 reference statements)

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“…These signals are captured with an electromyograph, which integrates an instrumentation amplifier that captures and amplifies the EMG signal with the help of three reference electrodes (Fang et al, 2020). This signal is sent through a data acquisition board (DAQ), to a processing system where it is processed and sent to a data acquisition system (DAS) (Gohel & Mehendale, 2020) to a processing system where it is subjected to extraction methods that perform the analysis of the signal (Ahsan et al, 2009;Zabala et al, 2019). Sodhi et al (2019) correlated bite EMG signals with texture variables (instrumental and sensory) of seven Indian sweets, identifying EMG parameters that distinguish the different textured foods.…”

Section: Electromyographic Analysismentioning

confidence: 99%

Technological tools for the measurement of sensory characteristics in food: A review

2023

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The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector.

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Section: Electromyographic Analysismentioning

confidence: 99%

Technological tools for the measurement of sensory characteristics in food: A review

2023

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“…Airwriting is the process of writing letters in free space using unrestricted finger movements [7]- [10]. EMG is a physiological signal generated due to muscle contraction and relaxation during the movement [11]. EMG signal can be acquired in two ways: (a) intramuscular EMG, where electrodes are inserted into the skeletal muscle and (b) surface EMG, where the electrodes are placed on the skin above the muscle.…”

Section: A Backgroundmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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Airwriting Recognition is the task of identifying letters written in free space with finger movement. It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and userfriendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different timefrequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in userdependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.

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“…The main bioelectrical signals are generated by the heart, the brain and the muscles, producing ECGs, EEGs and EMGs, respectively. The acquisition, analysis and interpretation of these signals are fully reported in literature [24][25][26][27][28]. Electrical signal variations generate EMG signals during muscular activities [68].…”

Section: Emg Signal Acquisition: General Considerationsmentioning

confidence: 99%

“…ECG, EEG and EMG signals are characterized by low amplitude (generally, expressed in mV-millivolts) and low operating frequencies, from frequency Hz to some kHz range [23]. The acquisition, analysis and interpretation of these signals are fully reported in the literature [24][25][26][27][28][29][30][31]. Physical activity is often associated with the cardiovascular and muscular systems.…”

Section: Introductionmentioning

confidence: 99%

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Palumbo

Vizza

Calabrese

et al. 2021

Sensors

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Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

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Review on electromyography signal acquisition and processing

Cited by 52 publications

References 36 publications

Technological tools for the measurement of sensory characteristics in food: A review

Technological tools for the measurement of sensory characteristics in food: A review

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

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