scholarly journals An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3108 ◽  
Author(s):  
Shing-Hong Liu ◽  
Chuan-Bi Lin ◽  
Ying Chen ◽  
Wenxi Chen ◽  
Tai-Shen Huang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Root Mean Square ◽  
The Body ◽  
Median Frequency ◽  
Mean Square ◽  
Sport Injuries ◽  
Physiological Measurement ◽  
The Real ◽  
Emg Signal

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

Joint analysis of spectrum and amplitude (JASA) of electromyograms applied for the indication of muscular fatigue in carpet weaving

2019 ◽  
Author(s):  
Narges Mortazavi ◽  
Teimour Allahyari ◽  
Hamid Reza Khalkhali ◽  
Mohammad Ali Sanjari
Keyword(s):  
Muscle Fatigue ◽  
Joint Analysis ◽  
Separate Analysis ◽  
Median Frequency ◽  
Mean Square ◽  
Spectral Parameters ◽  
Upper Trapezius ◽  
Emg Signal ◽  
Repetitive Tasks ◽  
High Prevalence

Abstract Background Hand carpet weaving requires prolonged static and repetitive tasks in a stationary and constrained postures, thus there is a high prevalence of muscle pain and fatigue and musculoskeletal disorders among carpet weavers.Methods This study aimed to determining the muscle fatigue in carpet weaving tasks by Joint Analysis of Spectral and amplitude (JASA (method. Two typical carpet weaving workstations were simulated in laboratory, and 12 female carpet weavers participated in this study. Electromyography (EMG) signals were recorded during work in bilateral upper trapezius and bilateral middle deltoid. The root mean square (RMS) and median frequency (MF) values were extracted from the raw EMG signal and used to estimate muscle load and fatigue. Joint analysis of spectral and amplitude (JASA (was performed for estimating the muscle fatigue.Results Result showed that in three out of four tested muscles, muscle fatigue and force decrease in workstation B were higher than those in workstation A.Conclusion In comparison to the simultaneous analysis of amplitude and spectral parameters in JASA method, it seems that the results are more reliable than the separate analysis of spectral and amplitude parameters. However further studies on other involved muscles in carpet weaving tasks were recommended.

scholarly journals Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Sridhar P. Arjunan ◽  
Dinesh K. Kumar ◽  
Ganesh Naik
Keyword(s):  
Muscle Contraction ◽  
Muscle Fatigue ◽  
Root Mean Square ◽  
Linear Regression Analysis ◽  
Maximum Voluntary Contraction ◽  
Voluntary Contraction ◽  
Median Frequency ◽  
Mean Square ◽  
Surface Electromyogram ◽  
The Relationship

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study:normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P<0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P<0.01). Both of these features were not affected by the intersubject variations (P>0.05).

Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions

1990 ◽  
Vol 69 (5) ◽  
pp. 1810-1820 ◽  
Author(s):  
R. Merletti ◽  
M. Knaflitz ◽  
C. J. De Luca
Keyword(s):  
Muscle Fatigue ◽  
Root Mean Square ◽  
Conduction Velocity ◽  
Time Course ◽  
Tibialis Anterior Muscle ◽  
Median Frequency ◽  
Mean Square ◽  
Myoelectric Signal ◽  
Physiological Factor ◽  
Voluntary Contractions

The time course of muscle fiber conduction velocity and surface myoelectric signal spectral (mean and median frequency of the power spectrum) and amplitude (average rectified and root-mean-square value) parameters was studied in 20 experiments on the tibialis anterior muscle of 10 healthy human subjects during sustained isometric voluntary or electrically elicited contractions. Voluntary contractions at 20% maximal voluntary contraction (MVC) and at 80% MVC with duration of 20 s were performed at the beginning of each experiment. Tetanic electrical stimulation was then applied to the main muscle motor point for 20 s with surface electrodes at five stimulation frequencies (20, 25, 30, 35, and 40 Hz). All subjects showed myoelectric manifestations of muscle fatigue consisting of negative trends of spectral variables and conduction velocity and positive trends of amplitude variables. The main findings of this work are 1) myoelectric signal variables obtained from electrically elicited contractions show fluctuations smaller than those observed in voluntary contractions, 2) spectral variables are more sensitive to fatigue than conduction velocity and the average rectified value is more sensitive to fatigue than the root-mean-square value, 3) conduction velocity is not the only physiological factor affecting spectral variables, and 4) contractions elicited at supramaximal stimulation and frequencies greater than 30 Hz demonstrate myoelectric manifestations of muscle fatigue greater than those observed at 80% MVC sustained for the same time.

Assessment of EGNOS Model over Asia Area Using IGS_ZTD

2014 ◽  
Vol 501-504 ◽  
pp. 2182-2186
Author(s):  
Li Long Liu ◽  
Miao Zhou ◽  
Teng Xu Zhang ◽  
Wei Wang ◽  
Liang Ke Huang
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Tropospheric Delay ◽  
Mean Square ◽  
Correction Model ◽  
The Real ◽  
Zenith Tropospheric Delay ◽  
Temporal And Spatial ◽  
Delay Correction

In this study, three years of the zenith tropospheric delay (ZTD) data observed from 46 International GNSS system (IGS) sites distributed in Asian area used to assess the effectiveness and accuracy of ZTD calculated from EGNOS model, and the application of the EGNOS model are also analyzed in Asian area. Relative to IGS observed ZTD, the bias and root mean square (RMS) for ZTD calculated from EGNOS model presents an obvious variation in temporal and spatial. These results provide a reference for the study of the tropospheric delay correction model, the real-time GNSS navigation and positioning.

scholarly journals Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study

2020 ◽  
Vol 65 (4) ◽  
pp. 461-468
Author(s):  
Jannatul Naeem ◽  
Nur Azah Hamzaid ◽  
Amelia Wong Azman ◽  
Manfred Bijak
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Isometric Force ◽  
Mean Square ◽  
Clear Indication ◽  
Initial Value ◽  
Cord Injury ◽  
The Mean ◽  
Electrical Stimulator ◽  
Fatigue Detection

AbstractFunctional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

scholarly journals A fractional order epidemic model for the simulation of outbreaks of Ebola

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Weiqiu Pan ◽  
Tianzeng Li ◽  
Safdar Ali
Keyword(s):  
Numerical Solution ◽  
Root Mean Square ◽  
Fractional Order ◽  
Relative Error ◽  
Numerical Solutions ◽  
Real Data ◽  
Mean Square ◽  
Order Model ◽  
The Real ◽  
Fractional Orders

AbstractThe Ebola outbreak in 2014 caused many infections and deaths. Some literature works have proposed some models to study Ebola virus, such as SIR, SIS, SEIR, etc. It is proved that the fractional order model can describe epidemic dynamics better than the integer order model. In this paper, we propose a fractional order Ebola system and analyze the nonnegative solution, the basic reproduction number $R_{0}$ R 0 , and the stabilities of equilibrium points for the system firstly. In many studies, the numerical solutions of some models cannot fit very well with the real data. Thus, to show the dynamics of the Ebola epidemic, the Gorenflo–Mainardi–Moretti–Paradisi scheme (GMMP) is taken to get the numerical solution of the SEIR fractional order Ebola system and the modified grid approximation method (MGAM) is used to acquire the parameters of the SEIR fractional order Ebola system. We consider that the GMMP method may lead to absurd numerical solutions, so its stability and convergence are given. Then, the new fractional orders, parameters, and the root-mean-square relative error $g(U^{*})=0.4146$ g ( U ∗ ) = 0.4146 are obtained. With the new fractional orders and parameters, the numerical solution of the SEIR fractional order Ebola system is closer to the real data than those models in other literature works. Meanwhile, we find that most of the fractional order Ebola systems have the same order. Hence, the fractional order Ebola system with different orders using the Caputo derivatives is also studied. We also adopt the MGAM algorithm to obtain the new orders, parameters, and the root-mean-square relative error which is $g(U^{*})=0.2744$ g ( U ∗ ) = 0.2744 . With the new parameters and orders, the fractional order Ebola systems with different orders fit very well with the real data.

scholarly journals Power factor correction using capacitors & filters

2018 ◽  
Vol 7 (2.12) ◽  
pp. 234
Author(s):  
Karthik Subramanian ◽  
Shantam Tandon
Keyword(s):  
Root Mean Square ◽  
Phase Difference ◽  
Power Factor ◽  
Reactive Power ◽  
Power Factor Correction ◽  
Active Filters ◽  
Minimum Amount ◽  
Mean Square ◽  
The Real ◽  
Electrical Consumption

Power factor is the ratio of the real current or voltage received by a load to the root mean square (rms) value of the current or voltage that was supposed to be acquired by the same load. The fact that the two become different is due to the presence of reactive power in the circuit which gets dissipated.Improving the power factor means reducing the phase difference between voltage and current. Since majority of the loads are of inductive nature, they require some amount of reactive power for them to function. Therefore, for the better use of electrical appliances with minimum amount of electrical consumption, the power factor should necessarily be increased and should be brought near to 1. This can be easily done by the help of Automatic Power Factor Correction Capacitors and Active filters.  

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

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