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Analysis of optimal muscle fatigue index using multichannel surface electromyogram data acquisition system.


Carrying heavy loads is one of the most common reasons behind the leading cause of musculoskeletal injuries. Peoples employed in specific occupations often have to carry heavy loads. The latest 3 year averaged estimates from the Labour Force Survey (LFS) showed an estimated 54000 reportable handling injuries resulted in more thanthree days absence from work, this was a rate of 190 per 100000 workers (LFS).Surface electromyography (SEMG), has been used in both research and clinical applications for non-invasive neuromuscular assessment in several different fields such as sport science [1], neurophysiology and rehabilitation. SEMG signals are electrical signals associated with the contraction of muscle, acquired non-invasively using electrodes placed on the surface of the skin above the muscle of interest. SEMG can be used to determine the relationship between muscle activation signals and biomechanical variables. It is a valuable tool for the assessment of muscle load and fatigue. SEMG in gait analysis is useful for patients suffering from neuromuscular diseases. SEMG signal amplitude is on the order of a few millivolts, therefore, SEMG acquisition requires specialized bioinstrumentation amplifiers with high input impedance, high common mode rejection ratio, and low noise.

Fatigue can be induced by prolonged or vigorous activities that include physical or mental effort, like severe exercise, prolonged physiological stress and chronic diseases. Muscular fatigue can be regarded as a reduced muscular work capacity, together with the loss of efficiency, often accompanied by subject feeling of physical and mental fatigue. In the study of human biomechanics, it is often desirable to assess the fatigue of muscles that are involved in performance of a task [2,3]. Physiologists have become accustomed to using the force output of a muscle as the index of muscle fatigue. In particular, the point at which a contraction can no longer be maintained (the failure point) is generally the point at which the muscle is said to fatigue. This approach implies that fatigue occurs at a specific point in time [5]. There are various methods of the EMG signal analysis. For many years the commonly used indicators of load and fatigue were determined in time or frequency domain on the basis of Fourier transform.


In this paper, a EMG amplifier circuit is designed and SEMG signals acquired to monitor and evaluate the changes in EMG parameters of muscles of low extremities. The recording is done while performing muscle activity carrying load. The acquired signal is then analysed both in time domain as well as frequency domain to estimate muscle fatigue. Fig. 1 shows the flow diagram for estimating the performance system.

A. Data Acquisition:

The EMG signal is acquired using 4 channel EMG amplifier. The pre-processing of the EMG signals includes the amplification of the signal, bandpass filtering of the signal followed by rectification of the signal. Signal acquisition has been performed by means of circular disposable Ag/Ag-Cl gel electrodes. Proper skin preparation is important to get good signal and avoid artefacts. The Electrode-Skin interface generates a DC voltage which can be minimized with proper skin preparation. The skin surface is cleaned by acetone with help of tissue paper. After skin preparation the electrode placement is carried on.

EMG sinal is accquired using the designed EMG amplifier board (20-500Hz) and is digitized using PIC microcontroller.

B. Protocol:

Seven healthy Subjects both male and female of age (23-25) participated in this study. Subjects details like age, height, weight and BMI was collected. According to the details, the load was calculated for each subject for each test. Experimental procedure was explained to the subjects. After all procedure were explained to them, Skin site was prepared and the electrodes are placed on the site. Subjects were asked to perform 3 walking tests. Initially with no load then for the second test a backpack load of 10% of their BW was given. Then third test was with 15% of their BW. Subjects were asked to walk and the last 30s of walking was recorded. EMG signal is acquired for all three varying loads and analysed using software to find the desired result.

C. EMG Features:

The acquired EMG signal is of RAW type. The signal has a wide frequency spectrum and the most complete information. The difficulty in using this data is that it requires large memory space for data storage. The received also may contain noise and as a result it is difficult to determine the levels of fatigue and the onset times, but they can be extracted from it using processing techniques.

In order to determine the effect of muscle fatigue on the EMG signal characteristics, the differences in the values of parameters at the end and at the beginning of the tests were analysed. For each participant, each level of load and each muscle tested.

The commonly used indicators of load and fatigue were determined in time or frequency domain on the basis of Fourier transform. The assessment of muscle load in time domain on the basis of the amplitude is RMS( root mean square).

The EMG analysis for muscle fatigue is commonly performed through spectral parameters median frequency (MDF) over time during a fatiguing effort. The percentage of fatigue during exercise is determined by the ratio between the variation in the median frequency.

The RMS of the measured EMG signal is calculated using (1).

RMS = [square root of (1/N [N.summation over (n=1)] [x.sub.n.sup.2])]

Where [x.sub.n] is the voltage value at ith sampling and N is the number of the samples in a segment.

The next feature, MDF is the frequency of EMG signal at which the average power within the segment/windows is reached. MPF can be calculated as in (2).

EMG is analysed in frequency domain to find the presence of fatigue. The frequency domain parameter like median frequency is used to find onset of fatigue during muscle activity

[[integral].sup.MDF.sub.0] p(f) df = [[integral].sup. [infinity].sub.MDF] p(f)df = 1/2 [[integral].sup. [infinity].sub.0] p (f)df

Where MDF is the median frequency value and P(f) is the PSD of the signal.


The acquired EMG signal for Gastrocnemius, Tibialis Anterior muscles are shown in the Figure [3],

EMG signal of each subject is taken for three different loads and the median frequency is plotted.RMS and Median frequency for each trial is estimated and plotted graphically.

(a) Without Load

(b) With 10% Load

(c) With 15% Load

From the above graphs it is clearly presented that muscle fatigue causes decrease in MDF values which is caused by shifts of the power spectral density (PSD) of the EMG signal towards relatively lower frequencies.

The effects of muscle fatigue on both EMG features RMS and MDF were analyzed by comparing the variations for each load.

RMS value for each load

MDF values for each load

The results of EMG RMS feature has increased after the fatiguing with load. Whereas the MDF feature has been shifted to lower frequencies with respect to the same angle of motion after the subject performed the fatiguing protocol. The percentage variation of MDF values for each subject is calculated and its also compared gender wise. By comparing the percentage variation of MDF feature we can estimate the onset of muscle fatigue, thereby optimal load for each subject is estimated.


An EMG amplifier circuit comprising of four channels was designed and developed to determine the effect of increasing backpack loads of 0%, 10% and 15% of BW. The acquired EMG signal is analysed for muscle fatigue. This project utilises the RMS and MDF of EMG to determine the onset of occurrence of fatigue. Muscle fatigue causes an increase in RMS and decrease in the values of MDF. From the analysis it is seen that a 15% BW of load could potentially be an appropriate safe backpack load limit for a male and a 10% of BW of load is safe for a female to reduce load-carriage related injuries which impair performance and cause discomfort and disability. Further studies should also test heavier percentage of BW backpack loads to compare the effects of heavy and lighter backpacks. The prolonged effect of backpack loads requires testing by walking for longer durations.


[1.] Adam Freed, Adrian D.C. Chan, Edward D. Lemaire, AviParush, 2013. "Wearable EMG Analysis for Rehabilitation (WEAR) Surface electromyography in clinical gait analysis", IEEE 978-1-4799-0698-7/13.

[2.] Ananda Sankar Kundu, Oishee Mazumder and Subhas is Bhaumik International, 2011. "Design of Wearable, Low Power, Single Supply Surface EMG Extractor Unit for Wireless Monitoring", Conference on Nanotechnology and Biosensors IPCBEE vol.25 IACSIT Press.

[3.] Igor Luiz Bernardes de Moura, Luan Carlos de Sena Monteiro Ozelim, Fabiano Araujo Soares, 2014. "Low Cost Surface Electromyographic Signal Amplifier Based On Arduino Microcontroller", International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, p: 8.

[4.] Jamaluddin, S.A., S.B.N. Ahmad, Noor, W.Z.W. Hasan, 2014. "Low cost and wearable multichannel surface electromyography data acquisition system architecture", Journal of Engineering Science and Technology Special Issue.

[5.] Jingpeng Wang, Liqiong Tang, John E Bronlund, 2013. "Surface EMG Signal Amplification and Filtering", International Journal of Computer Applications (0975-8887) 82: 1.

[6.] Marco, A.C. Garcia, Joao M.Y. Catunda, Thiago Lemos, Liliam F. Oliveira, Luis A. Imbiriba and Marcio N. Souza, 2010. Member, IEEE "An Alternative Approach in Muscle Fatigue Evaluation from the Surface EMG Signal", 32nd Annual International Conference of the IEEE EMBBS.

[7.] Navaneethakrishna, M and S. Ramakrishnan, 2014." Multiscale Feature Based Analysis of Surface EMG Signals under Fatigue and Non-fatigue Conditions", IEEE978-1-4244-7929-0/14.

[8.] Rendek, M., Daricek, E. Vavrinsky, M. Donoval and D. Donoval, 2010. "Biomedical signal amplifier for EMG wireless sensor system", IEEE978-1-4244-8575-8/10.

[9.] Thiago, V. Camata, Jose L. Dantas, TaufikAbrao, Maria A.O.C. Brunetto, Antonio C. Moraes, Leandro R. Altimari, 2010. "Fourier and Wavelet Spectral Analysis of EMG signals in Supramaximal Constant Load Dynamic Exercise", 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina,.

[10.] Youngjin Lee, Youngjoon Chee, 2013. "Evaluation of the effectiveness of muscle assistive device using muscle fatigue analysis", 35th Annual International Conference of the IEEE EMBS Osaka, Japan.

(1) M. Prathisha, (2) B. Suresh Chander Kapali, (3) Jason Jebasingh, (4) Annette Shalom Rakshana and (4) Preethi. M.

(1,2,3) Alpha College of Engineering,, Anna University, Department of Biomedical Engineering, Assistant Professor, Chennai, TamiiNadu, India

(4) Alpha College of Engineering,, Anna University, Department of Biomedical Engineering, U. G Students, Chennai, TamilNadu, India.

Received 28 January 2017; Accepted 12 May 2017; Available online 18 May 2017

Address For Correspondence:

M. Prathisha, Alpha College of Engineering,, Anna University, Department of Biomedical Engineering, Assistant Professor, Chennai, TamilNadu, India


Caption: Fig. 1: Wear system architecture

Caption: Fig. 3: Raw EMG Signal

Caption: Fig. 4: MDF Value for each load

Caption: Fig. 5: RMS and MDF values for each subject
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Author:Prathisha, M.; Kapali, B. Suresh Chander; Jebasingh, Jason; Rakshana, Annette Shalom; Preethi, M.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Date:May 1, 2017
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