Non-invasive techniques to assess muscle fatigue using biosensors: A review

Sarillee, Mohamed; Hariharan, M.; Anas, M.N.; Omar, Mohammad Iqbal; Aishah, M.N.; Oung, Q.W.

doi:10.1109/icsgrc.2014.6908719

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…MMG has been used and validated in quantifying muscle fatigue [23] during isometric as well as dynamic contractions [2,24] and clinical applications [25]. It employs different types of transducers to measure the mechanical property of muscle derived from the motor unit's activity [26] and it is not easily influenced by skin impedance or artefacts from other probes [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy

Saadon

Hamzaid

Hasnan

et al. 2019

Biomedical Engineering / Biomedizinische Technik

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Repetitive electrically-evoked muscle contraction leads to accelerated muscle fatigue. This study assessed electrically-evoked fatiguing muscle with changes to mechanomyography root mean square percentage (%RMS-MMG) and tissue saturation index (%TSI) in extensor carpi radialis. Forty healthy volunteers (n=40) performed repetitive electrical-evoked wrist extension to fatigue and results were analyzed pre- and post-fatigue, i.e. 50% power output (%PO) drop. Responses of %PO, %TSI and %RMS-MMG were correlated while the relationships between %RMS-MMG and %TSI were investigated using linear regression. The %TSI for both groups were negatively correlated with declining %PO as the ability of the muscle to take up oxygen became limited due to fatigued muscle. The %RMS-MMG behaved in two different patterns post-fatigue against declining %PO whereby; (i) group A showed positive correlation (%RMS-MMG decreased) throughout the session and (ii) group B demonstrated negative correlation (%RMS-MMG increased) with declining %PO until the end of the session. Regression analysis showed %TSI was inversely proportional to %RMS-MMG during post-fatigue in group A. Small gradients in both groups suggested that %TSI was not sensitive to the changes in %RMS-MMG and they were mutually exclusive. Most correlation and regression changed significantly post-fatigue indicating that after fatigue, the condition of muscle had changed mechanically and physiologically.

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Section: Introductionmentioning

confidence: 99%

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy

Saadon

Hamzaid

Hasnan

et al. 2019

Biomedical Engineering / Biomedizinische Technik

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“…However, the combination of myograms gives a much better result than EMG. It is shown that, the combination of three myograms provides more useful information, which cannot be obtained from single-modal system [30].…”

Section: Resultsmentioning

confidence: 98%

Classification of muscle fatigue condition using multi-sensors

Sarillee

Hariharan

Anas

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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The aim of this work is to assess the muscle fatigue condition using multimodal system. Muscle fatigue is a common muscle condition which experiences in our daily activity. There were 20 subjects participated in this study. Electromyogram (EMG) (shows the electrical activity of the muscle), Mechanomyogram (MMG) (shows a mechanical activity of the muscle) and Acoustic myogram (AMG) (is audible produced when the muscle was contracted) were used in this study. EMG, MMG and AMG were recorded continuously from hamstring muscle, according to the data acquisition protocol. The recorded signals were segmented into fatigue and non-fatigue. Time domain, frequency domain and time-frequency domain features were extracted from the myograms. The extracted features were classified using k-nearest neighbor. The mean accuracy of EMG, MMG and AMG was 87.10%, 81.40% and 67.23% respectively. The mean accuracy of the multimodal system was 92.07%. In this paper, we also have discussed the effect of single myogram and multi modal myograms.

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“…However, the parameters of the inverse models vary strongly among individuals, with time, and depending on the task, which makes robust impedance control challenging [8,9]. Alternative measurement technologies for estimation of the interactive human force/torque include sonomyography [10,11], optics [12], mechanomyography [13], magnetomyography [14], surface electromyography (EMG), and bioimpedance or electrical impedance myography (EIM).…”

Section: Introductionmentioning

confidence: 99%

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography

Ngo

Muñoz

Lueken

et al. 2022

Sensors

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The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20–150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

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Non-invasive techniques to assess muscle fatigue using biosensors: A review

Cited by 8 publications

References 15 publications

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy

Classification of muscle fatigue condition using multi-sensors

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography

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