A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

Toledo-Pérez, Diana C.; Rodríguez‐Reséndiz, Juvenal; Gómez‐Loenzo, Roberto A.

doi:10.1109/access.2020.2964678

Cited by 61 publications

(32 citation statements)

References 47 publications

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“…As also reported in [42], [43], ATC and other similar techniques, e.g., Zero-Crossing (ZC), Wilson Amplitude (WAMP), fit particularly well to the sEMG signal because of their amplitude proportionality and incremental variability in presence of muscle activity (i.e., bio-inspired). Thus, the ATC parameter could be designed as a useful indicator for monitoring the muscle behavior.…”

Section: Average Threshold Crossing: a Journey Into The Last Decade Of Our Researchsupporting

confidence: 73%

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

et al. 2021

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Device wearability and operating time are trending topics in recent state-of-art works on surface ElectroMyoGraphic (sEMG) muscle monitoring. No optimal trade-off, able to concurrently address several problems of the acquisition system like robustness, miniaturization, versatility, and power efficiency, has yet been found. In this tutorial we present a solution to most of these issues, embedding in a single device both an sEMG acquisition channel, with our custom event-driven hardware feature extraction technique (named Average Threshold Crossing), and a digital part, which includes a microcontroller unit, for (optionally) sEMG sampling and processing, and a Bluetooth communication, for wireless data transmission. The knowledge acquired by the research group brought to an accurate selection of each single component, resulting in a very efficient prototype, with a comfortable final size (57.8 mm x 25.2 mm x 22.1 mm) and a consistent signal-to-noise ratio of the acquired sEMG (higher than 15 dB). Furthermore, a precise design of the firmware has been performed, handling both signal acquisition and Bluetooth transmission concurrently, thanks to a FreeRTOS custom implementation. In particular, the system adapts to both sEMG and ATC transmission, with an application throughput up to 2 kB s −1 and an average operating time of 80 h (for high resolution sEMG sampling), relaxable to 8 B s −1 throughput and about 230 h operating time (considering a 110 mAh battery), in case of ATC acquisition only. Here we share our experience over the years in designing wearable systems for the sEMG detection, specifying in detail how our event-driven approach could benefit the device development phases. Some previous basic knowledge about biosignal acquisition, electronic circuits and programming would certainly ease the repeatability of this tutorial.

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Section: Average Threshold Crossing: a Journey Into The Last Decade Of Our Researchsupporting

confidence: 73%

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

et al. 2021

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“…ZC represents the number of times the signal crosses the zero value in a given time period. It is used to provide an approximation of the frequency domain [32] and can be used to estimate the fatigue in the muscles [33]. The count of ZC is incremented when:…”

Section: Methodology a Feature Extractionmentioning

confidence: 99%

Electromyography-Based Decoding of Dexterous, In-Hand Manipulation of Objects: Comparing Task Execution in Real World and Virtual Reality

et al. 2021

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The increased use of Virtual and Augmented Reality based systems necessitates the development of more intuitive and unobtrusive means of interfacing. Over the last years, Electromyography (EMG) based interfaces have been employed for interaction with robotic and computer applications, but no studies have been carried out to investigate the continuous decoding of the effects of human motion (e.g., manipulated object behavior) in simulated and virtual environments. In this work, we compare the object motion decoding accuracy of an EMG based learning framework for two different dexterous manipulation scenarios: i) for simulated objects handled by a teleoperated model of a hand within a virtual environment and ii) for real, everyday life objects manipulated by the human hand. To do that, we utilize EMG activations from 16 muscle sites (9 on the hand and 7 on the forearm). The object motion decoding is formulated as a regression problem using the Random Forests methodology. A 5-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each model. The decoding accuracy for the real world is considerably higher than the virtual world. Each of the objects examined had a single manipulation motion that offered the highest estimation accuracy across both worlds. This study also shows that it is feasible to decode the object motions using just the myoelectric activations of the muscles of the forearm and the hand. This is particularly surprising since simulations lacked haptic feedback and the ability to account for other dynamic phenomena like friction and contact rolling.

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“…There are various techniques with numerous complexity in time and frequency domain, which shows different FEMG characteristics [5]. For feature extraction, we implemented a WT method that generates wavelet coefficients [29][30][31][32][33][34][35][36]. An active part of FEMG data containing 8000 samples with a sampling frequency of 1000Hz.…”

Section: Feature Extractionmentioning

confidence: 99%

A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM

2020

IJE

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A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

Cited by 61 publications

References 47 publications

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Electromyography-Based Decoding of Dexterous, In-Hand Manipulation of Objects: Comparing Task Execution in Real World and Virtual Reality

A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM

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