Non-invasive electromyography-based fatigue

2012 IEEE International Conference on Control System, Computing and Engineering, 23 - 25 Nov. 2012, Penang, Malaysia

Non-Invasive Electromyography-Based Fatigue Detection and Performance Analysis on m. biceps brachii Muscle N. Ahamed1, 2, *, K. Sundaraj1, 2, RB. Ahmad3, M. Rahman4, A. Islam1, 3, A. Ali1, 3 1

AI-Rehab Research Group, Universiti Malaysia Perlis, Malaysia School of Mechatronic Engineering, Universiti Malaysia Perlis, Malaysia 3 School of Computer and Communication Engineering, Universiti Malaysia Perlis, Malaysia 4 College of Computer Science and Information System, Najran Univeristy, Kingdom of Saudi Arabia * [email protected] 2

In our daily activities, the upper arm biceps brachii muscle can become highly stressed; therefore, painful disorders can develop after long periods of repetitive and intense exercise. It is difficult to diagnose the muscle fatigue than the knowing of it. Many different definitions of muscle fatigue exist. For example, according to Gonçalves et al., muscle fatigue is a failure to maintain a preferred level of yield or work during a repetitive or continued activity [11]. Santos et al., mentioned that muscle fatigue has a multi-factor etiology and its source as well as extension depend on the specificity of the exercise, type of muscle fiber involved and degree of physical fitness [12]. Oksa defined it as a state reached through expanded and exhaustive muscle contraction [13]. However, these entire descriptions share the common feature that muscle fatigue is decreased the skill of motor function ability which causes a decline in muscle performance [1, 8, 14-17]. The arm wrestling game is performed by the participant’s upper arm and several types of muscles including biceps brachii are involved to produce the force during the competition. It is an effortless contest during which the results of the winner and loser can be determined within a short time [18]. Moreover, two competitors use their single arm to generate the maximum effort to win the game and place their elbow on a hard surface. During the game, competitor clasp each other’s palm and tries to push the other’s arm until it hits the surface [19]. Here, two contractions were produced from the participators throughout the game. First one was the eccentric contraction from the loser when the biceps was forced, the forearm was being pronated and elbow was gradually being extended. Another one was the concentric contraction which was generated by the winner and biceps muscles were contracted during the activities. As a result, our main concern was to analysis the muscle fatigue during these two contractions simultaneously. Earlier studies presented different types of fatigue detection techniques and analysis process during muscle contraction on biceps brachii muscles. For example, Walker et al., discussed fatigue detection on vastus lateralis, vastus medialis and biceps femoris muscle during isometric and eccentric contraction [20]. Dimitrova et al., analyzed fatigue on m. biceps brachii during isometric contraction at the different level of MVC% [21]. The main principle of Soylu’s

Abstract— Muscle fatigue occurs most frequently due to repetitive movements in our day-to-day activities. The powerful use of muscles might cause a decline in performance. In this study, the effects of muscle fatigue involving only the m. biceps brachii muscle in the dominant upper arm were investigated during the course of an arm wrestling game where the muscle contractions were produced by the winner and loser, respectively. This competition was conducted to monitor the progress/decline of muscle strength among young players. Eight male subjects (age: 26±3 years, height: 170±13 cm, weight: 70±13 kg and BMI: 21±4) were participated in this study. Electrodes were placed on the middle of the biceps muscle to record the electromyography (EMG) signals. Each group (two players) participated twice with each other, and the EMG signals were recorded. Muscle fatigue was analyzed and compared using the first-time and second-time EMG data. The results were evaluated by calculating the average EMG, root mean square (RMS) and the highest peak of the signal during muscle contraction. Among these, RMS and highest peak values helped in efficiently assessing muscle fatigue. Most of the results indicated that m. biceps muscle reduced its strength during the second game; also, winners performance were found to be better than the losers (according to EMG amplitudes). The current findings regarding the m. biceps brachii muscle might prove to be useful in developing effective injury prevention and rehabilitation strategies, and other physiological measurements related to the upper arm muscles. Index Terms—Electromyography, fatigue, m. biceps brachii, muscle contraction, arm wrestling.

I. INTRODUCTION For the last 60-70 years, analysis of electromyography (EMG) signals has been widely used to provide a fuller understanding of the muscle function and dysfunction during the movement of the human body [1]. EMG signals have multifarious characteristics: they are very random, nonstationary, nonlinear, complex and are not generated by periodic phenomena. However, surface EMG (sEMG) has been used as a noninvasive diagnostic tool in several research areas, sports science and clinical disciplines [2-4]. Moreover, sEMG is the most appropriate signal processing technique for the detection, monitoring and analysis of muscle function and fatigue during contraction [5-10]

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et al., study was to find the sEMG time interval of the maximum voluntary contraction (MVC) recordings which was not affected by the biceps brachii muscle fatigue [8]. In summary, quite a number of literatures have looked into the muscle fatigue on the biceps brachii but none of them explored during an arm wrestling of this muscles. To date, much of the fatigue-related research in biceps brachii has focused on the investigation of muscle activity patterns using sEMG [22]. However, we could not find any related papers where fatigue of the m. biecps brachii muscle was investigated during eccentric-concentric contraction. Arm wrestling is a good example of an activity where these two contractions are produced simultaneously. The current study concisely presents the existing and novel approaches for assessment of the movement of the human m. biceps brachii muscle, which is important for sports science, ergonomics, physiological measurement and other biomedical concerns related to the upper extremities.

C. Procedures During the contest, two players were requested to sit on the chairs and a small table was in between them. Two rings were drawn on the table in front of each player. Each pair of competitors participated two times to decide the winner and loser. Both the participants kept their right elbow inside the round on the table. Their palms were attached to each other as well as left arms were folded along the back of their bodies (Fig 1). All the constitutions were followed by the world arm wrestling federation (WAF), which is located in Canada (http://www.worldarmwrestlingfederation.com). A referee was present to conduct the game and the game was started upon the referee’s whistle. There was no time limit for the game and the referee kept track of the total time as the two participants attempted to press on their arms in the direction of their left and cause their opponent’s arm to fall on the table. The winner was therefore the participant that successfully pushed his opponent’s arm to the table, i.e., the winner’s palm is on top of the loser’s. There was two minutes rest between each game.

II. METHODS A. Subjects Eight right-hand dominant healthy subjects participated in the experiment and they have given their written consent. Anthropometric characteristics of the participants (arm wrestlers) are presented in Table I. The university research committee was approved the experimental design. TABLE I. General Characteristics of the Subjects Variables

Age (years)

Height (cm)

Weight (kg)

Body mass index (BMI)

Mean Standard deviation (SD)

26 3

170 5

70 13

21 4

B. Instrumentation Electromyography (EMG) responses from m. biceps muscle was recorded at a sample rate 1000 Hz using a wireless, touch proof and Bluetooth-enabled three channels connected EMG signal DAQ system, called SHIMMERTM Model SH-SHIM-KIT-004 (Real-time Technologies Ltd., Ireland). AG/AgCL (Meditrace noninvasive electrodes) active sensors were used during the experiment. Two sensors were placed at the middle of the biceps brachii muscle and the reference one was attached at the back of the elbow (bony area). Distance between center to center of the sensors was 20 mm. Before this, the skin of the biceps muscle was prepared using a skin cleaning gel (sigma gel) and an alcohol swab to obtain better EMG signals and avoid any artifacts. The skin was prepared and the electrodes were placed in accordance with Zipp et al., and SENIAM recommendations [23, 24]. Two Bluetooth enabled high configured laptop were used to record the EMG signal from two participants.

Fig.1. Illustration of EMG signal recording process during the competition. Electrodes were placed on the middle of the biceps muscle.

D. Statistical analysis: All summary statistics are presented as the mean± standard deviation (SD). Data analysis was calculated as off line data. Mean±SD, root mean square and highest peak signal analysis values were considered to get the effective results. EMG values from the first-time competition were compared with second-time data. The MedCalc (MedCalc® Version 11.3.0.0) statistical software was used for the EMG data analyze. III. RESULTS Tables II and III show the electromyographic signal results from m. biceps brachii muscle in winners and losers of an arm wrestling contest. Mean (±standard deviation) amplitude of the first and second EMG signal was similar during the first and second muscle contractions; the two phases of EMG data did not differ significantly, suggesting that mean EMG amplitude is not an optimal measurement for assessing muscle fatigue. Furthermore, the overall EMG value during the first

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and second contraction was the same in winners and losers (2.1mV, Table III). When the data from the first and second contractions were combined, winners showed slightly higher activity than losers (0.1 mV). Calculation of the root mean square (RMS) of the recorded EMG signal allowed us to efficiently assess muscle fatigue. The recorded EMG signal value was lower during the second contraction than during the first contraction in 75% of cases, in both losers and winners. The mean amplitude difference was ±2.0 mV based on the RMS value (Table II). In general, the whole calculation indicated that second-time data were lower than first-time data, and winners produced greater EMG signals than losers. Our last analysis variable was the maximum peak value of the generated EMG signal. The results also followed same as like RMS value. The average differences between first- and second-time EMG signals were ±2.0 mV. According to the overall calculation, winner generated more signals than the losers.

• In the loser, the biceps was forced to perform an eccentric contraction as the forearm was pronated and as the elbow was gradually extended. Conversely, contraction of the biceps was concentric in the winner. • As a result, two contractions were generated during the competition. One was a concentric contraction in the winner and the other was an eccentric contraction in the loser. • Winners (during concentric contractions) did not always produce the highest EMG signals and some of the results (muscle activity) we obtained with the losers (during eccentric contraction) were higher than those obtained with the winners. • Figure 2 represented the physiological performance and fatigue of the individual games. Mean, average RMS and maximum peaks of the EMG signals were calculated to estimate the physiological performance and fatigue during individual games. Average EMG (mean) showed that performance was the same for both the competitors. The amplitude was ±2.0mV, indicating that the same amount of muscle activity was produced during eccentric and concentric contractions. However, average values were inadequate for detecting fatigue in this way; thus, RMS and max peak were used to identify muscle fatigue. • Taken together, the results suggest that the combined use of several physiological measurement methods yields a better portrait of the different signs of muscle fatigue during contraction. However, a careful selection of evaluation methods, protocols, subjects and analysis tools are required, particularly when more complex muscle contractions and sports are studied. • Lastly, indications of upper arm m. biceps brachii muscle fatigue is essential to understand work and sports related musculoskeletal damages and progress working performance. The results of the current study are applicable to comparisons of other upper arm muscles. Also, it will carry as a reference for physical therapy, repetitive tasks and any other biomedical concerns with varying muscle contraction.

IV. DISCUSSION In this study we analyzed the muscle fatigue during arm wrestling or it can be described as during the eccentricconcentric contraction from m. biceps muscle. Muscle fatigue is a multifaceted and complicated development in the human body that involves various physiological, biomechanical and psychological components [25]. A number of studies have addressed fatigue of the biceps brachii muscle [26-29]. In the present study, muscle fatigue was examined by measuring the mean, root mean square and the maximum peak value of the m. biceps brachii muscle, whereas muscle strength was assessed by recording EMG signals recorded during arm wrestling. The following results were revealed from our study: • Fatigue of the biceps brachii muscle was detected under dynamic conditions (contraction). The EMG signals were lower on the second measurement than on the first measurement, indicating that muscle fatigue had occurred between the two measurements (RMS and max amplitude). • Figure 3 illustrated a clear picture where it showed the winner in two-arm wrestling generated more EMG signals than the loser. Also, for both the competitors’ second time data were lower than the first-time data. This figure was depicted on the basis of RMS value.

TABLE II. INDIVIDUAL EMG RESULTS FROM THE PARTICIPANTS Game 1 Variable Mean±SD RMS Max. PK

Winner st

1 2.0 2.9 4.0

nd

2 2.1 2.9 4.0

Game 2

Loser

st

1 2.1 2.1 2.9

nd

2 2.1 1.9 2.7

Winner

st

1 2.1 2.6 3.6

nd

2 2.1 2.4 3.4

st

Game 3 Loser

1 2.1 2.4 3.4

nd

2 2.1 2.3 3.2

304

Winner

st

1 2.1 2.4 3.4

nd

2 2.1 2.5 3.5

Game 4 st

Loser

1 2.1 2.5 3.5

nd

2 2.1 2.4 3.4

Winner st

1 2.1 2.5 3.5

nd

2 2.1 2.3 3.2

Loser

1st 2.1 1.9 2.8

2nd 2.1 2.6 2.6


V. CONCLUSION TABLE III. COMBINED ELECTROMYOGRAPHIC (EMG) RESULTS FROM THE

The potential of this analysis will be credible after estimating the existing methods, which have the greatest possibility to be characteristically used as reliable muscle fatigue measures on m. biceps. Based on the results of the present study; in the maximum cases winners generated more electromyographic signals than the losers, muscle fatigue was best detectable from the RMS and maximum peak analysis during the arm wrestling, average EMG values were inadequate for detecting fatigue in this way, and both the eccentric and concentric contractions were performed during arm wrestling. Although, the results obtained in this study were specific to find the fatigue and performance from m. biceps brachii muscle, however it needs more investigation in terms of different protocols. Lastly, the findings proposed that the combined use of several physiological measurement methods yields a better portrait of the different signs of muscle fatigue during contraction. However, a careful selection of evaluation methods, protocols, subjects and analysis tools are required, particularly when more complex muscle contractions and sports are studied

WINNERS AND LOSERS MUSCLE

Overall results Analysis of Mean±SD RMS Max. PK

Winners 1st 2nd

Losers 1st 2nd

2.1 3.1 3.5

2.1 2.5 4.0

2.1 2.8 3.4

2.1 2.3 4.1

Winners Both rounds

Losers Both rounds

2.2 3.1 4.1

2.1 2.5 3.5

A. Limitations As a major aim of the study was to evaluate the physiological activity of the upper arm biceps brachii muscle (both performance and fatigue), there were some limitations in this study: number of subjects were few, we have investigated only right arms muscle, chosen only male subjects and few channels connected with data acquisition system. Lastly, we have analyzed the statistical effects instead of broad signal processing technique. However, our future investigation will fulfill all these gaps.

REFERENCES [1] W. M. Pereira, L. A. B. Ferreira, L. P. Rossi, I. I. Kerpers, L. A. C. Grecco St, A. R. de Paula Jr, and C. S. Oliveira, "Influence of heat on fatigue and electromyographic activity of the biceps brachii muscle," Journal of Bodywork and Movement Therapies, vol. 15, pp. 478-484, 2011. [2] J. Duchene and F. Goubel, "Surface electromyogram during voluntary contraction: processing tools and relation to physiological events," Crit Rev Biomed Eng, vol. 21, pp. 313-97, 1993. [3] V. Gupta, S. Suryanarayanan, and N. P. Reddy, "Fractal analysis of surface EMG signals from the biceps," International Journal of Medical Informatics, vol. 45, pp. 185-192, 1997. [4] N. U. Ahamed, K. Sundaraj, R. B. Ahmad, M. Rahman, A. Islam, and A. Ali, "Analysis of the Effect on Electrode Placement on an Adolescent’s Biceps Brachii during Muscle Contractions Using a Wireless EMG Sensor," Journal of Physical Therapy Science, vol. 24, pp. 609-611, 2012. [5] M. González-Izal, A. Malanda, I. Navarro-Amézqueta, E. M. Gorostiaga, F. Mallor, J. Ibañez, and M. Izquierdo, "EMG spectral indices and muscle power fatigue during dynamic contractions," Journal of Electromyography and Kinesiology, vol. 20, pp. 233-240, 2010. [6] A. Nsenga Leunkeu, D. J. Keefer, M. Imed, and S. Ahmaidi, "Electromyographic (EMG) Analysis of Quadriceps Muscle Fatigue in Children With Cerebral Palsy During a Sustained Isometric Contraction," Journal of Child Neurology, vol. 25, pp. 287-293, March 1, 2010 2010. [7] A. Subasi and M. Kiymik, "Muscle Fatigue Detection in EMG Using Time–Frequency Methods, ICA and Neural Networks," Journal of Medical Systems, vol. 34, pp. 777-785, 2010. [8] A. R. Soylu and P. Arpinar-Avsar, "Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction," Journal of Electromyography and Kinesiology, vol. 20, pp. 773-776, 2010.

Fig. 2. Identification of muscle fatigue among two recorded (first and second) EMG data, in terms of RMS (Root Mean Square) calculation

Fig. 3. EMG (mV) analysis in terms of individual games, winner (W) and loser (L). Filled markers represent first time data and without filled markers represent second time data.

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2012 IEEE International Conference on Control System, Computing and Engineering, 23 - 25 Nov. 2012, Penang, Malaysia [9] N. U. Ahamed, K. Sundaraj, R. B. Ahmad, M. Rahman, A. Islam, and M. A. Ali, "Analysis of right arm biceps brachii muscle activity with varying the electrode placement on three male age groups during isometric contractions using a wireless EMG sensor," Procedia Engineering, vol. 41C, pp. 61-67, 2012. [10] A. U. Nizam, S. Kenneth, R. B. Ahmad, M. Rahman, A. Ali, and A. Islam, "Electromyographic Responses during Elbow Movement at Two Angles with Voluntary Contraction: Influences of Muscle Activity on Upper Arm Biceps Brachii," Research Journal of Applied Sciences, Engineering and Technology, vol. 4, pp. 4591-4595, 2012. [11] M. Goncalves, "Electromyography and identification of muscle fatigue," Revista Brasileira de Educação Física e Esporte/ Brazilian Journal of Physical Education and Sport, vol. 20, pp. 91-93, 2006. [12] M. G. d. Santos, V. H. Dezan, and T. A. Sarraf, "Metabolic basis of muscle fatigue acute," Revista Brasileira de Ciência & Movimento, vol. 11, pp. 7-12, 2003. [13] J. Oksa, M. B. Ducharme, and H. Rintamäki, "Combined effect of repetitive work and cold on muscle function and fatigue," Journal of Applied Physiology, vol. 92, pp. 354-361, January 1, 2002 2002. [14] R. M. Enoka and J. Duchateau, "Muscle fatigue: what, why and how it influences muscle function," The Journal of Physiology, vol. 586, pp. 11-23, 2008. [15] D. G. Allen and H. Westerblad, "Role of phosphate and calcium stores in muscle fatigue," The Journal of Physiology, vol. 536, pp. 657-665, 2001. [16] L. A. C. Kallenberg, E. Schulte, C. Disselhorst-Klug, and H. J. Hermens, "Myoelectric manifestations of fatigue at low contraction levels in subjects with and without chronic pain," Journal of Electromyography and Kinesiology, vol. 17, pp. 264274, 2007. [17] N. A. Dimitrova and G. V. Dimitrov, "Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies," J Electromyogr Kinesiol, vol. 13, pp. 13-36, 2003. [18] L. Gang, C. Haifeng, and L. Jungtae, "A Prediction Method of Muscle Force Using sEMG," in Computer Science and Information Technology - Spring Conference, 2009. IACSITSC '09. International Association of, 2009, pp. 501-505. [19] ArmWrestling, "Canada," [Update 2004; cited 2012 March 15]. vol. Available from: http://www.armwrestling.com, 2004.

[20] S. Walker, L. Davis, J. Avela, and K. Hakkinen, "Neuromuscular fatigue during dynamic maximal strength and hypertrophic resistance loadings," J Electromyogr Kinesiol, vol. 22, pp. 35662, 2012. [21] N. A. Dimitrova, T. I. Arabadzhiev, J. Y. Hogrel, and G. V. Dimitrov, "Fatigue analysis of interference EMG signals obtained from biceps brachii during isometric voluntary contraction at various force levels," Journal of Electromyography and Kinesiology, vol. 19, pp. 252-258, 2009. [22] S.-Y. Le, S.-C. Kim, M.-H. Lee, and J.-S. Yoo, "Effect of the Height of a Wheelchair on the Shoulder and Forearm Muscular Activation During Wheelchair Propulsion," Journal of Physical Therapy Science, vol. 24, pp. 51-53, 2012. [23] H. J. Hermens, B. Freriks, R. Merletti, D. Stegerman, J. Block, and G. R. e. al., "SENIAM: European recommendations for surface electromyography Roessingh Research and Development, Enschede," http://www.seniam.org/, 1999. [24] P. Zipp, "Recommendations for the standardization of lead positions in surface electromyography," European Journal of Applied Physiology and Occupational Physiology, vol. 50, pp. 41-54, 1982. [25] J. Seghers and A. Spaepen, "Muscle fatigue of the elbow flexor muscles during two intermittent exercise protocols with equal mean muscle loading," Clinical Biomechanics, vol. 19, pp. 2430, 2004. [26] J. Langenderfer, S. LaScalza, A. Mell, J. E. Carpenter, J. E. Kuhn, and R. E. Hughes, "An EMG-driven model of the upper extremity and estimation of long head biceps force," Computers in biology and medicine, vol. 35, pp. 25-39, 2005. [27] P. Ravier, O. Buttelli, R. Jennane, and P. Couratier, "An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions," J Electromyogr Kinesiol, vol. 15, pp. 210-21, 2005. [28] V. Srhoj-Egekher, M. Cifrek, and V. Medved, "The application of Hilbert–Huang transform in the analysis of muscle fatigue during cyclic dynamic contractions," Medical and Biological Engineering and Computing, vol. 49, pp. 659-669, 2011. [29] I. Stirn, T. Jarm, V. Kapus, and V. Strojnik, "Evaluation of muscle fatigue during 100-m front crawl," European Journal of Applied Physiology, vol. 111, pp. 101-113, 2011.

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