“Electromyography Segmented Assessment for Lower …“Electromyography Segmented Assessment for Lower Limb Muscle Transition to Fatigue During Isometric Contraction” (#) 1 By

“Electromyography Segmented Assessment for Lower Limb Muscle

Transition to Fatigue During Isometric Contraction”

1 (#)

By Jorge Garza-Ulloa, Pablo Rangel, Olatunde Adeoye

Department of Electrical and Computer Engineering,

University of Texas at El Paso, Laboratory for Human Motion Analysis and Neurorehabilitation,

El Paso, USA

“Electromyography Segmented Assessment for Lower Limb Muscle Transition to Fatigue During Isometric Contraction”

Outline

1. Abstract

2. Introduction and Background

3. Concept: Transition-to-fatigue

4. Methodolgy / Experimentation

6. Results

7. Conclusions (

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Abstract ( Olatunde Adeoye )

Surface Electromyography (sEMG) activity of the lower extremities Analyze muscle transition to fatigue on four leg muscles. Measured lower extremity muscles :

• vastus lateralis (VL) • gastrocnemius lateralis (GL) • Tibialis anterior (TA) • Soleus (S)

Data recorded when subjects performed an isometric exercise : • one minute test twice for energize muscles • two and three minutes for stimuli transition-to-fatigue

Objective: determine and analyze the muscle transition to fatigue activity generated by muscle tension for a specified period of time. sEMG data of the subjects segmented for fast analysis and easy tracking of the changes observed on raw data without any filtering.

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Amplitude of the high frequency is correlated to force level Amplitude of the low frequency band is correlated to the muscle fatigue. Two methods were used:

• Amplitude row data related with raw sEMG data , MNF during the segmentation • Analyzing the muscles with more activities on all test using segmentation.

•Results show the effects of transition-to-fatigue phenomena contributed to: • Physiological fatigue • Muscle fatigue.


Abstract continue...

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Introduction and Background Localized muscle fatigue occurs:

• After a prolonged relatively muscle activity • When muscle or groups of muscles are unable to exert any more

force or power. (Physiological fatigue). Three types of localized muscles are:

• Non- fatigue • Transition-to-fatigue • Fatigue

sEMG signal can be analyzed to detect Transition-to-Fatigue. Development in muscle fatigue correlates to: Changes in amplitude and Median Mean Frequency ( MNF ) The signal is analyzed in frequency-domain

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Concept: Transition to fatigue

Simple muscle twitch •Mechanical response of a single muscle fiber to a single action potential •Latent period (10 ms): Muscle tension is beginning. •Contraction period (40 ms): Muscle fibers shorten. •Relaxation period (50 ms): Ca2+ renters the sarcoplasmic reticulum •Total duration -100 ms (varies) •Doesn’t have refractory period

http://howmed.net/wp-content/uploads/2010/09/membrane-action-potential.bmp

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“Electromyograhic Segmented Assessment for Lower Limb Muscle Transition to Fatigue During Isometric Contraction”

Concept: Transition-to-fatigue

Summation Adding together of individual twitch contractions to increase the intensity of overall muscle contraction 1) Multiple fiber summation 2) Frequency summation

http://howmed.net/wp-content/uploads/2010/09/frequency-summation.bmp

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Multiple fiber summation •More the fibers (motor units) taking part in contraction more will be the force of contraction •For weak contraction, smaller and fewer motor units are stimulated •For stronger contractions more & more motor units are stimulated (recruitment) •Frequency summation, tetanus or tetanization •Sustained contraction due to repeated stimuli of high frequency

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Frequency summation •Incomplete tetanization

•Muscle fiber is stimulated at such a frequency that every next stimulus falls during previous relaxation period •Subsequent contraction is superimposed on the previous relaxation •Force of subsequent contractions rises due to beneficial effect of Ca++ •Muscle fiber partially relaxes between stimuli

•Complete tetanization •Muscle fiber is stimulated at such a frequency that every next stimulus falls during previous contraction period •Subsequent contractions merge with the previous ones •Smooth contraction of greater force is achieved •No relaxation phase •Leads to fatigue

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Muscle fatigue: Decrease in muscular activity due to repeated stimuli Causes: a. In muscle

•Lack of nutrients and glycogen •Lack of oxygen •Accumulation of lactic acid •Conduction failure along ‘T’ tubules -blockage of Ca2+release for sarcoplasmic cistern

b. In Neuromuscular junction •Depletion of Acetyl Choline

c. In Central Neural System •CNS cannot send excitatory signals to the contracting muscles •Generally psychological •Fatigue reverses by taking rest

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Methodolgy / Experimentation

Main objective :

• Get the subjects into a state of incomplete tetanus to analyze the trends of how the Transition-to-Fatigue happens. • Dangerous to have them reach a state of complete tetanus so the exercises were design to keep them from any harm.

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Methodolgy / Experimentation Experimental Procedure •At detail the procedure was explained to every participant. •An IRB (Institutional Review Board) agreement was signed by each subject before proceeding with the experimentation. •Preliminary anthropometric, demographic, and clinical data was taken from each subject. •The Pocket EMG by BTS Bioengineering was properly calibrated and placed over the subjects.

Software and System Set Up •The Pocket EMG by BTS Bioengineering sEMG System software was configure and initialized following the User’s Guide directions and the previous training provided to the laboratory staff.

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Sensor Placement •The Pocket EMG by BTS Bioengineering electrodes were allocated over the following muscles:

• Vastus Lateralis (VL) by knee force, • Gastrocnemius Lateralis (LG) as flexion of knee • Soleus (Sol) as an antagonist plantarflexion • Tibialis Anterior (TA) as as Dorsiflexion

Before placing the sensor areas where cleaned and shaved (if needed) to obtained a better and direct contact to the muscle.

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“Electromyograhic Segmented Assessment of Lower Limb Muscle Transition to Fatigue During Isometric Contraction”


Trials Complete procedure took about thirty minutes. Subjects were able to dictate when they were ready to proceed They were given a rest period of at least 5 minutes between each trial. The experimental procedure consisted of one isometric exercise procedure been repeated four times in three different time ranges:

•First (1MA), a sEMG reading of the legs at the assigned position was acquired for one minute. •Next, the subject was asked to do the same isometric exercise for one more minute (1MB), then two minutes (2M) and finally three minutes (3M).

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Procedure

Our goal analyze the sEMG with all possible detail of muscle change, huge files ! • Raw Data from Sample Frequency of 2000 Hz • Aprox. 120,000 samples per minute

• No filter of any kind • The Raw data was segmented each 5,000

samples, this means each segment of 2.5 sec. to register any muscle variation.

• Find Spectrum frequency using fast Fourier transform (FFT)

• Calculate Mean Frequency (MNF)

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Procedure:

Anthropometric Data Tests:

SUBJECT AGE SEX HEIGTH

(cm) WEIGTH ( Kg) BMI 1MA 1MB 2M 3M

SH1 22 MALE 177 95.34 30.4 Y Y i X

SH2 32 MALE 177 93.07 29.7 Y Y Y i

SH3 24 MALE 180 85.81 26.5 Y Y Y Y

SH4 23 FEMALE 167 68.00 25.5 Y Y Y i

SH5 26 FEMALE 162 70.00 26.8 Y Y Y Y

SH6 19 FEMALE 168 71.00 25.2 Y Y Y Y

SH7 21 MALE 155 65.83 27.4 Y Y Y Y

SH8 27 MALE 177 88.53 28.2 Y Y Y Y

SH9 29 MALE 177 90.80 29 Y Y Y i

SH10 21 MALE 179 90.80 28.3 Y Y i x

SH11 42 MALE 167 83.99 30.1 Y Y i x

SH12 34 MALE 183 129.39 38.9 Y Y i x

12 subjects with IRB (Institutional Review Board) agreement signed

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Two analysis methods used

1) Amplitude segmented sEMG row data related with Mean Median Frequency (MNF)

2) MNF changes on the muscles with more activities (all segmented tests)

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Method 1 Amplitude segmented row data related with MNF

• Raw Data obtained from the sensor as: sum of many Motor Unit Action Potential (MUAP) using:

where x(n) is modeled EMG signal, e(n) is point processed represents the firing impulse, h(r) represents the MUAP, w(n) zero mean addictive white Gaussian noise and N is the number of motor unit firings. To see all portion of raw data the MNF was calculated from fast Fourier transform (FFT) that computes the discrete Fourier transform (DFT) as:

Where 𝑥0, … , 𝑥𝑁−1 𝑎𝑟𝑒 𝑐𝑜𝑚𝑝𝑙𝑒𝑥 𝑛𝑢𝑚𝑏𝑒𝑟,

𝑁 𝑜𝑢𝑡𝑝𝑢𝑡𝑠, 𝑘 = 0, … , 𝑁 − 1 • MNF as: .

distribution of frequencies from fmin to fmax, each with amplitude A(f) and a frequency f

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Method 1: Amplitude segmented sEMG row data related with MNF

More information is obtained from the sEMG signal working on the Spectrum Frequencies !

sEMG Signal

Frequency

Time

Rectified/Filtered

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Method 1: Amplitude segmented sEMG row data related with Mean Frequency (MF)

Amplitude of frequencies are correlated to force level and the amplitude of low frequency is correlated to muscle fatigue

Shift to

lower freq.

Conclusions:

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Method 2: (Jorge Garza-Ulloa)

Charting MF changes on the muscles with more activities on all segmented tests

Fact: The muscle that show highest activity on this isometric tests were

• Vastus Lateralis (VL)

• and Gastrocnemius(G)

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60

65

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0 20 40 60 80

Fre

q. (

Hz)

Segments

SH03- Vastus Lateralis Left

1MA

1MB

2MA

3M65

70

75

80

85

90

95

0 20 40 60 80

Fre

q. (

Hz)

Segments

SH03- Vastus Lateralis Right

1MA

1MB

2MA

3M

55

60

65

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75

80

85

90

95

0 10 20 30 40 50 60

Fre

q. (

Hz)

Segments


1MA

1MB

2MA

3M55

60

65

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75

80

85

90

95

0 10 20 30 40 50 60

Fre

q. (

Hz)

Segments


1MA

1MB

2MA

3M

50

55

60

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0 5 10 15 20 25 30

Fre

q. (

Hz)


1MA

1MB

2MA

50

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0 5 10 15 20 25 30

Fre

q. (

Hz)

Segments


1MA

1MB

2MA

Tests 1MA 1MB 2M 3M

SH01 Y Y I X

Tests 1MA 1MB 2M 3M

SH02 Y Y Y I

Tests 1MA 1MB 2M 3M

SH06 Y Y Y Y

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Conclusions Two methods were used to study the muscle transition to fatigue: 1. Amplitude segmented row data related with mean Median Frequency (MF) “The subject must generate more magnitude of voltage ( Force ) to maintain the frequency and when less magnitudes are generated are on the muscle transition to fatigue steps (accumulation of MUAP is reaching its limits )”

2. MF changes on the muscles with more activities on all segmented tests “ show clear frequency shift to lower values when more time is tested as Dif. Freq =fmax-fmin, higher average, more variance and negative slope”

Transition to fatigue

SH12 1MA 1MB 2MA 3M

AVERAGE 64.70 67.91 69.40

VARIANCE 3.68 4.36 6.97

STD DESV 1.92 2.09 2.64

SLOPE 0.19 -0.23 -0.15

Dif. Freq 7.42 8.31 14.04 0.00

3. Results test suggests : • People that make exercise regularly show a longer period to muscle Transition-to

Fatigue and even can correct some irregularity . • Males may experience greater muscle fatigue than female shows.

4. Muscle are different for each subjects then the transition to fatigue vary according with a big numbers of variables that can influence like: age, anthropometric data, alcohol levels, caffeine, smoke, exercise, different kind of food and many more.

This assessment based on segmentation of sEMG shows promising results for detecting and predicting muscle transition to fatigue. More research is needed before be applied on different human-computer interaction as automated system like:

• Sport to improve performance or prevent injury • Ergonomics to improve postures • Prosthetics to feedback how to avoid unnecessary strain on the muscle to prevent

more injury • Special jobs where is a risk for any musculoskeletal injury.

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Conclusions

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References: [1] Merletti, R.; Parker, P.A. Electromyography: Physiology, Engineering and Non-Invasive Applications; John Wiley and sons, Inc.: New York, NY, USA, 2004. [2] Mair, S.D.; Seaber, A.V.; Glisson, R.R.; Garrett, W.E. The role of fatigue in susceptibility to acute muscle strain injury. Am. J. Sport. Med. 1996, 24, 137–143. [3] Al-Mulla, M.R.; Sepulveda, F.; Colley, M.; Al-Mulla, F. Statistical class separation using sEMG features towards automated muscle fatigue detection and prediction. In Proceedingsof International congress on image and signal processing, Tianjin, China, 7–19 October 2009; pp. 1–5. [4] Herberts, P.; Kadefors, R.; Broman, H. Arm positioning in manual tasks. An electromyographic study of localized muscle fatigue. Ergonomics 1980, 23, 655–665. [5] M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin. Techniques of emg signal analysis: detection, processing, classification and applications. Biological Procedures Online, 2006. [6] Calder, K.M.; Stashuk, D.W.; McLean, L. Physiological characteristics of motor units in the brachioradialis muscle across fatiguing low-level isometric contractions. J. Electromyograph. Kinesiol. 2008, 18, 2–15. [7] Mohamed R. Al-Mulla , Francisco Sepulveda and Martin Colley. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors 2011, ISSN 1424-8220 [8] Alexander RM, Bennet-Clark HC. 1977. Storage of elastic strain energy in muscle and other tissues. Nature 265:114–117. [9] Marieb, Book: Human Anatomy & Physiology 5th edition, Benjamin Cummings, San Francisco 2001 [10]Sheir, Butler, & Lewis Hole, Book: Human Anatomy 10th edition McGraw Hill, Boston 2004

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“Electromyography Segmented Assessment for Lower …“Electromyography Segmented Assessment for Lower Limb Muscle Transition to Fatigue During Isometric Contraction” (#) 1 By