Suppression of Motion Artifacts in Multichannel Mechanomyography Using Multivariate Empirical Mode Decomposition

Wang, Daqing; Wu, Huijun; Xie, Chenlei; Gao, Lifu

doi:10.1109/jsen.2019.2906218

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Even if an ulterior verification was done on kinematics between intensity levels, artifacts such as muscular tremor or higher-frequency motion harmonics might have altered the MMG signal. In future work, to address these limits, we could improve MMG and EMG signal processing to better filter motion artifacts in dynamic conditions using EMD (Empirical Mode Decomposition)-based algorithms, which showed more effective results than bandpass filtering recently [11,12,36]. Other limits of the current study are related to gender as only male subjects have been selected.…”

Section: Limitsmentioning

confidence: 98%

Mechanomyographic analysis for muscle activity assessment during a load-lifting task

Correa¹,

Projetti²,

Siegler³

et al. 2023

Preprint

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The purpose of this study was to compare Electromyographic (EMG) with Mechanomyo- 1 graphic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. 2 Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and 3 submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants 4 then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, 5 at 25%, 50% and 75% of their maximum activity (based on MVC). The low frequency part of the 6 accelerometer signal (< 5 Hz) was used to segment the 6 phases of the motion. EMG and MMG were 7 both recorded during the entire experimental procedure. Root mean square (RMS) and mean power 8 frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG 9 and MMG exhibited similar repeatability scores. They also shared similar RMS vs force relationship, 10 with RMS increasing to 75% MVC and plateaued to 100%. MPF decreased with increasing force to 11 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than 12 RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional 13 activities outside the laboratory. It opens new perspectives for future applications in sports science, 14 ergonomics and human-machine interfaces conception.

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Section: Limitsmentioning

confidence: 98%

Mechanomyographic analysis for muscle activity assessment during a load-lifting task

Correa¹,

Projetti²,

Siegler³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Most studies used a band-pass filter between 5 and 100 Hz to eliminate both motion artifacts and high-frequency noise [ 8 ]. Recently, advances in MMG signal processing methods have led to more efficient motion artifact filtering in dynamic conditions [ 11 , 12 ]. Being sensitive to motion could also be viewed as a strong argument in favor of accelerometer-based MMG, as the low-frequency part of the signal (motion accelerations) can be used as a segmenting tool [ 13 ], making the most out of the overall signal.…”

Section: Introductionmentioning

confidence: 99%

Mechanomyographic Analysis for Muscle Activity Assessment during a Load-Lifting Task

Correa,

Projetti,

Siegler

et al. 2023

Sensors

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The purpose of this study was to compare electromyographic (EMG) with mechanomyographic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, at 25%, 50% and 75% of their maximum activity (based on MVC). The low-frequency part of the accelerometer signal (<5 Hz) was used to segment the six phases of the motion. EMG and MMG were both recorded during the entire experimental procedure. Root mean square (RMS) and mean power frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG and MMG exhibited similar repeatability scores. They also shared similar RMS vs. force relationship, with RMS increasing to 75% MVC and plateauing to 100%. MPF decreased with increasing force to 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional activities outside the laboratory. It opens new perspectives for future applications in sports science, ergonomics and human–machine interface conception.

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“…NMES has been identified as an alternative tool to voluntary activation in order to generate muscle contraction; it has been determined to be an important feature to selectively activate desired muscles. Nevertheless, the changes in EMG patterns over time [ 3 , 4 ] and electrical interference [ 5 ] are considered deterministic factors that might affect its proper interpretations when the targeted muscle is subjected to electrical quantities. Consequently, the development of piezoelectric, microphones, and accelerometers showed appropriate detection of the mechanical signals from the surface of skeletal muscles at a low frequency, which is known as MMG signal [ 6 ] not contaminated by electrical noise.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the development of piezoelectric, microphones, and accelerometers showed appropriate detection of the mechanical signals from the surface of skeletal muscles at a low frequency, which is known as MMG signal [ 6 ] not contaminated by electrical noise. Indeed, MMG signal represents the mechanical manifestation of muscle activity [ 7 ] and further indicates the neurophysiology reflected by the mechanical counterpart to the electrical activity of unfused active motor units [ 5 ]. Attempts made in the assessment of crosstalk [ 8 ], quantification [ 9 ], and applications in assistive technology [ 10 ] support MMG signal as an alternative to EMG signal for the screening of muscle function [ 11 ], in terms of fatigue [ 12 ], muscle force [ 13 ], and its derivative (torque) [ 14 ] as well as for prosthesis control [ 15 ] and the detection of myopathies [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

2021

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This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.

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Suppression of Motion Artifacts in Multichannel Mechanomyography Using Multivariate Empirical Mode Decomposition

Cited by 11 publications

References 35 publications

Mechanomyographic analysis for muscle activity assessment during a load-lifting task

Mechanomyographic analysis for muscle activity assessment during a load-lifting task

Mechanomyographic Analysis for Muscle Activity Assessment during a Load-Lifting Task

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

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