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1
Content available remote Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal
Shariatzadeh Mohammad Javad, Hadizadeh Hafshejani Ehsan, Mitchell Cameron J., Chiao Mu, Grecov Dana
Biocybernetics and Biomedical Engineering
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2023
|
Vol. 43, no. 2
428--441
EN
Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties. In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction. Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue. The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.
2
Content available remote Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation
Wang Ying, Wu Qun, Dey Nilanjan, Fong Simon, Ashour Amira S.
Biocybernetics and Biomedical Engineering
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2020
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Vol. 40, no. 3
987--1001
EN
Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental results proved the importance of the sEMG signals for extracting such reactions, in which, the segmentation and classification of the sEMG are vital tasks. The objective of the present work is to segment and classify the sEMG signals of patients to assist the design of clinical rehabilitation devices based on the classification of sEMG signals. In the pre-processing stage, a dual-tone multi-frequency signaling is designed for signal coding; subsequently, the pre-processed sEMG signal is transformed by the Fast Fourier Transfer. Afterward, a time-series frequency analysisis performed by applyingHiddenMarkov Models.A basic traditional longshort- term memory (LSTM) model is addressed for waveform-based classification to be compared to the proposed improved deep BP (back-propagation)–LSTM for sEMG signal classification. Seventeen performance features are selected for evaluating the proposed multi-classification, deep learning model for classifying six actions, namely moving gesture of grip, slowly moving, flexor, straight lift, stretch, and up-high lift; which were proposed by rehabilitation physician. The experiment results indicated the superiority of the proposed method compared to other well-known classifiers, such as the neural network, support vector machine, decision trees, Bayes inference, and recurrent neural network. The proposed deep BP–LSTM network achieved 92% accuracy, 89% specificity, 91% precision, and 96% F1-score, in the multi-classification of the sEMG signals.
3
Content available Komputerowe stanowisko pomiarowe z układem RLD do pozyskiwania sygnału elektromiograficznego
Krawiecki Zbigniew, Jędrych Bartłomiej, Hulewicz Arkadiusz, Dziarski Krzysztof
Poznan University of Technology Academic Journals. Electrical Engineering
|
2019
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No. 100
51--61
PL
W pracy przedstawiono wykonane komputerowe stanowisko pomiarowe do rejestracji sygnału bioelektrycznego z mięśni człowieka. Sygnał pozyskiwany jest metodą bezinwazyjną przy użyciu elektrod powierzchniowych. Do wzmocnienia sygnału z elektrod zastosowano wzmacniacz pomiarowy z obwodem Reg-Leg Drive (RLD) w sprzężeniu zwrotnym. Taka konstrukcja jest stosowana do pomiaru sygnału elektrokardiograficznego. W pracy autorzy wykorzystali ten układ do pomiaru sygnału elektromiograficznego (EMG). W torze pomiarowym zastosowana została karta DAQ sterowana przez magistralę USB. Przeprowadzone pomiary laboratoryjne wykazały poprawność działania wykonanego komputerowego stanowiska pomiarowego z układem Reg-Leg Drive do pozyskiwania sygnału elektromiograficznego.
EN
The article includes the description of the computer measurement station for recording the electrical signal from the muscle. The surface electrodes were used to obtain the EMG signal. The instrumental amplifier with the RLD circuit was used to strengthen the signal from the electrodes. The DAQ card was used for measurements and controlled by USB bus from the computer. The multiple measurement tests at rest and the muscle activity were carried out and the sample results were included in the article. It is assumed that the made measurement station will be used for the educational purposes in the field of the non-invasive biomeasurements.
4
Content available Changes in electromyographic signals and skin temperature during standardised effort in volleyball players
Kuniszyk-Jóźkowiak W., Jaszczuk J., Czaplicki A.
Acta of Bioengineering and Biomechanics
|
2018
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Vol. 20, no. 3
115--122
EN
The state of athletes’ muscles is not constant, but it differs depending on the stage of sports training, which is associated with different degrees of muscle fatigue. There is thus a need to find a non-invasive and simple method to assess muscle fatigue. The aim of the study was to determine the relationship between muscle fatigue due to physical effort and changes in skin temperature, measured using a thermographic camera. Methods: The study involved 12 volleyball players. The participants were to maintain 70% of peak torque in the joint for as long as possible. We measured peak torque and the time of maintaining 70% of its value (tlim) as well as continuously recording skin temperature and electromyographic (EMG) signals in the region of the belly of the rectus femoris. The measurements were taken twice: before and after a series of squats. Results: The study found that tlim decreased when isometric contraction was performed after physical effort. Pre- and post-exercise skin temperature did not differ significantly, however, the increase rates of temperature and the root mean square (RMS) of the EMG signals grew significantly. In most of the players, skin temperature also correlated with the RMS, median frequency (MDF), and mean frequency (MF) of the EMG signals. Conclusions: Measuring the time of maintaining submaximal torque during isometric contraction and the slope coefficient for the increase in temperature recorded using a thermographic camera can be a simple, cost-effective, and non-invasive method of assessing fatigue and efficiency decreases in the muscles in volleyball players.
5
Content available Przetwarzanie i analiza sygnału elektrycznego z mięśni przy użyciu komputerowego stanowiska pomiarowego
Krawiecki Z., Hulewicz A., Cichocki-Kaiser M.
Poznan University of Technology Academic Journals. Electrical Engineering
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2016
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No. 88
35--45
PL
Autorzy artykułu opisują przetwarzanie i analizę zmierzonego sygnału elektrycznego z mięśni w odniesieniu do budowy funkcjonalnej komputerowego stanowiska pomiarowego. Komputerowe stanowisko pomiarowe zostało wykonane z części sprzętowej i programowej jako wirtualny przyrząd pomiarowy. Część sprzętowa realizuje zadania przetwarzania sygnału takie jak: kondycjonowanie, filtrowanie, konwersja na postać cyfrową i wizualizacja danych. Część programowa to: formatowanie danych, analiza sygnału, filtracja cyfrowa, przygotowanie wyników pomiarów do wizualizacji i archiwizacji oraz sterowanie aparaturą. W pracy szczególną uwagę zwrócono na blok analizy sygnału elektromiograficznego i realizowane funkcje związane z usuwaniem artefaktów, wyznaczaniem wartości bezwzględnej oraz wartości średniej i skutecznej napięcia sygnału.
EN
The authors of the article describe the processing and the analysis of the measured electrical signal from the muscles in relation to the functional construction of the computer measurement system. The computer measurement system was made of hardware and software as a virtual instrument. The hardware performs signal processing tasks such as: conditioning, filtering, conversion to digital form and data visualization. The software includes: formatting data, the signal analysis, digital filtration, preparing the measurement results to visualization and archiving and controlling the equipment. In this article, the particular attention has been paid to the block of the electromyographic signal analysis and the implemented functions related to the removal of the artifacts, the determination of absolute value and the effective value of the voltage signal.
6
Content available Komputerowe stanowisko pomiarowe do rejestracji sygnału elektrycznego z mięśni
Krawiecki Z., Hulewicz A., Cichocki-Kaiser M.
Poznan University of Technology Academic Journals. Electrical Engineering
|
2016
|
No. 88
47--55
PL
Praca zawiera opis komputerowego stanowiska pomiarowego, w którym do pozyskania sygnału elektrycznego z mięśni wykorzystano elektrody powierzchniowe. Do wzmocnienia sygnału z elektrod zastosowano wzmacniacz instrumentalny. Następnie w torze sygnałowym użyty został filtr dolnoprzepustowy i karta pomiarowa DAQ. Sterowanie z poziomu komputera zostało zrealizowane z zastosowaniem magistrali USB. Do obsługi stanowiska oraz przetwarzania pozyskanych danych napisany został program w języku graficznym. Przeprowadzono wielokrotne pomiary testowe w stanie spoczynku i aktywności mięśni a przykładowe wyniki zostały zamieszczone w pracy. Zakłada się, że wykonane komputerowe stanowisko pomiarowe zostanie przeznaczone na cele dydaktyczne w zakresie nieinwazyjnych biopomiarów.
EN
The article includes the description of the computer measurement station, in which the surface electrodes were used to obtain the electrical signal from the muscle. The instrumental amplifier was used to amplify the signal from the electrodes. Then, in the signal path the low-pass filter and the measuring card DAQ were used. The computer controlling was realized by using the USB bus. The program was written in the graphic language to handle the position and the processing of acquired data. The multiple measurement tests at rest and the muscle activity were carried out and the sample results were included in the article. It is assumed that the made measurement station will be used for the educational purposes in the field of the non-invasive biomeasurements.
7
Content available Wpływ typu mięśnia na parametry widma mocy sygnału EMG
Bartuzi P., Roman-Liu D.
Aktualne Problemy Biomechaniki
|
2008
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z. 2
7--12
PL
Celem pracy było opracowanie wskaźników, opartych na widmie mocy sygnału elektromiograficznego (EMG), obrazujących budowę morfologiczną mięśnia. Sygnał EMG został zarejestrowany z mięśni: czworoboczny część zstępująca (trapezius pars descendens-TPD), naramienny część obojczykowa (deltoideus pars clavicularis-DPC), naramienny część barkowa (deltoideus pars acromialis-DPA) oraz zginacz łokciowy nadgarstka (flexor carpi ulnaris-FCU), podczas badań w 6 wariantach obciążenia. W wyniku analizy opracowano parametry widma mocy sygnału EMG, szczególnie wrażliwe na zmiany w strukturze mięśnia.
EN
The aim of this study was to develop coefficients of electromyographic (EMG) signal, which show the morphological properties of muscle. Measurements of EMG signal from muscles: trapezius pars descendens (TPD), deltoideus pars clavicularis (DPC), deltoideus pars acromialis (DPA) and flexor carpi ulnaris (FCU), during research in 6 variants of load were realized. As the result of analysis the parameters based on power spectrum of EMG signal were worked out. Differences between morphological structure of muscle can be described using these parameters.
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