ELECTROMYOGRAPHY

Neuromuscular function varies significantly among individuals due to factors including genetics, training history, age, and physiological characteristics. While basic EMG principles are well-established, the specific patterns of muscle fatigue, recovery, and individual variation in neuromuscular control remain active areas of research. This investigation addresses gaps in our understanding of how different feedback mechanisms and intervention strategies affect muscle performance across diverse populations.

RESEARCH QUESTION

How do individual differences in neuromuscular control strategies affect muscle fatigue patterns and recovery responses during sustained and repetitive contractions?

INDIVIDUAL RESEARCH PROJECTS

Each student team (2-3 students) will select ONE of the following specialized research focuses:

PROJECT A: FATIGUE PHENOTYPING

• Investigate whether individuals can be classified into distinct fatigue-resistance categories like "fast fatiguers," "slow fatiguers," or "maintainers"

• Develop fatigue indices based on EMG frequency analysis and force decline rates

• See if physical characteristics (arm size, activity level) can predict which fatigue type someone will be

Research Value: We don't actually know if people fall into distinct fatigue categories or exist on a continuum. Sports scientists and rehabilitation specialists would love to know this because it could revolutionize how we design training programs and therapy protocols.

PROJECT B: FEEDBACK OPTIMIZATION

• Test whether real-time EMG feedback (people can see their grip force, their muscle electrical activity, both, or neither) improves fatigue resistance

• Investigate optimal feedback modalities for different muscle activation strategies

• Develop personalized feedback protocols based on individual EMG patterns

Research Value: Current exercise equipment and rehabilitation devices mainly show force output. We don't know if adding EMG feedback would help people perform better or if different individuals respond better to different feedback types. This has direct applications for fitness technology and medical rehabilitation.

PROJECT C: REFLEX MODULATION STRATEGIES

• Explore alternative reinforcement techniques (mental math, squeezing opposite hand, etc.) beyond the Jendrassik maneuver (locking the hands together and pulls vigorously apart as a tendon in the lower extremity is tapped)

• Investigate how cognitive load affects reflex reinforcement effectiveness

• Test whether reflex sensitivity correlates with voluntary motor control patterns

Research Value: Reflex testing is crucial for diagnosing neurological problems, but we don't fully understand why enhancement techniques work or how to optimize them for different individuals. Better reflex testing could improve medical diagnosis and our understanding of nervous system function.

PROJECT D: RECOVERY DYNAMICS

• Extend protocols to include recovery phases between effort periods

• Investigate individual differences in neuromuscular recovery rates

• Correlate recovery patterns with fatigue resistance characteristics

Research Value: Most research focuses on fatigue but ignores recovery. Understanding individual recovery patterns could transform how we design workout schedules, work breaks, and rehabilitation protocols. This is particularly important for preventing overuse injuries and optimizing performance.

METHODOLOGY

1. Developing Your Hypothesis

Before data collection, complete this framework:

• I predict that...

• This should happen because...

• I will measure this by...

• I will know I'm right if...

2. Subject Information

For each team member who will serve as subject, complete a brief questionnaire:

• Age, biological sex, dominant hand

• Activity level (sedentary/moderate/active)

• Any hand/arm injuries or conditions

• Forearm circumference at largest point

• Hand length (wrist to middle fingertip)

3. Standard Protocol for All Projects

Electrode Placement:

• Clean skin with alcohol wipe

• Place electrodes: 5cm and 10cm from medial epicondyle on ventral forearm

• Reference electrode on upper arm

• Connect leads: red and green to forearm, black to upper arm

Position:

Seated, back straight, elbow at 90°, arm unsupported

Baseline Maximum Voluntary Contraction (MVC) Test:

• Subject grips dynamometer with maximum effort for 3 seconds

• Record peak force - this becomes their individual 100% MVC reference

• Rest 2 minutes between trials

• Repeat 3 times, use highest value as MVC

4. Core Protocol

Sustained Grip Test

Collection duration = 90 seconds, eyes closed initially

Phases:

• 0-30s: Sustain 80% of MVC (calculated from baseline)

• 30-60s: Continue effort, verbal encouragement at 45s

• 60-90s: Project-specific modifications

  • Project A (Fatigue Phenotyping): Continue sustained effort, monitor EMG frequency changes

  • Project B (Feedback): Open eyes, display real-time force + EMG feedback

  • Project C (Reflex): No modification, use standard protocol

  • Project D (Recovery): Release grip, monitor recovery for 30s

Visual Feedback Test

Collection duration = 90 seconds, eyes closed initially

Phases:

• 0-30s: Sustain 80% of MVC (calculated from baseline)

• 30-60s: Continue effort, verbal encouragement at 45s

• 60-90s: Project-specific modifications

Project-Specific Protocols:

1. Project A: Fatigue Classification Test

   • Repeat sustained grip at 60% MVC for 120 seconds

   • Focus on EMG frequency analysis during fatigue progression

2. Project B: Multi-Modal Feedback Test

   • Test combinations: force-only, EMG-only, combined feedback

   • 30-second trials for each feedback type

3. Project C: Reflex Enhancement Protocol

   • Standard patellar reflex testing (10 trials)

   • Enhanced reflexes with Jendrassik maneuver (10 trials)

   • Novel enhancement: mental math during reflexes (10 trials)

4. Project D: Recovery Characterization

   • Fatigue protocol followed by timed recovery assessment

   • Monitor EMG and force recovery over 5 minutes post-fatigue

5. Calculate Core Metrics

Descriptive Statistics (mean, SD, range)

Force:

• Mean force for each 30-second interval

• Fatigue Index = (Initial Force - Final Force) / Initial Force × 100

• Coefficient of Variation = Standard Deviation / Mean (force steadiness)

EMG:

• Peak EMG amplitude for each interval

• Mean EMG amplitude for each interval

• EMG-Force correlation coefficient

Individual Metrics:

• Express all forces as % of individual MVC

• Compare relative rather than absolute values

Visual Representations

Create graphs showing key relationships:

• Individual Profiles: Line graphs showing each person's fatigue/recovery pattern

• Group Comparisons: Bar charts comparing conditions or participant types

• Correlation Plots: Scatter plots showing relationships between variables

• Classification Results: Charts showing identified groups or categories

Identify Outliers

Note interesting individual patterns

Project-Specific Analysis:

• Project A: Cluster analysis to identify fatigue types

• Project B: Paired t-tests comparing feedback vs. no-feedback conditions

• Project C: Correlation analysis between reflex sensitivity and individual characteristics

• Project D: Recovery curve fitting and time constant calculation

INTERPRETING YOUR DATA

Ask these questions about your results:

1. What patterns do you see?

Look at individual data first, then group patterns

• Project A: As you watch fatigue develop, do you see distinct patterns emerging, or does everyone seem to fatigue differently?

• Project B: How do people change their behavior when they can see their performance feedback? Do they all respond the same way?

• Project C: When testing reflexes, what individual differences do you notice? Do enhancement techniques work equally well for everyone?

• Project D: During recovery periods, how quickly do force and EMG return toward baseline? Is recovery simply the reverse of fatigue?

2. Do the patterns support your hypothesis?

Be honest about what the data shows

• Project A Teams: If people do fall into fatigue categories, what factors do you think might determine which category someone belongs to? (genetics, training, age, etc.)

• Project B Teams: When might visual feedback help performance, and when might it hurt? Think of examples from sports, music, or other skilled activities.

• Project C Teams: The Jendrassik maneuver (interlocking fingers and pulling apart) somehow enhances reflexes. What theories can you develop for why this works?

• Project D Teams: Some people say they "bounce back" quickly from fatigue while others need longer to recover. What biological mechanisms might explain these differences?

• For all teams: How might your research findings be useful in the real world? Who would care about your results and why?

3. What could explain unexpected results?

Consider alternative explanations

• Your data probably looks "messier" than textbook examples. Why is this normal in research? How do you distinguish meaningful patterns from random variation?

• Have you encountered any unexpected results? What might explain findings that don't match your predictions?

• If you had to repeat this experiment, what would you do differently? What have you learned about research methodology?

4. How do your findings compare individual vs group results?

Place results in context

• How much of your results can be explained by group averages vs. individual differences? What does this suggest about personalized approaches to training or rehabilitation?

• Who were the "outliers" in each dataset, and what made them different? Are outliers just statistical noise, or might they represent important subpopulations?

• If you had to recommend training or rehabilitation strategies based on your findings, would you use a one-size-fits-all approach or try to personalize recommendations? Why?

REFLEX STUDY

A reflex may be reinforced (a term used by neurologists) by slight voluntary contraction of muscles other than the one being tested. For example, voluntary activation of arm muscles by motor neurons in the central nervous system "spills over" to cause a slight activation of the leg muscles as well. This results in the enhancement of the patellar reflex. There are other examples of central nervous system influences on reflexes. Health care professionals use knowledge of these influences to aid in diagnosis of conditions such as acute stroke and herniated lumbar disk, where reflexes may be absent; and spinal cord injury and multiple sclerosis, which may result in exuberant reflexes.

In this experiment, you will use an EMG sensor to compare the speed of a voluntary muscle action versus a reflex muscle action as well as to measure the relative strength (amplitude) of the impulse generated by a stimulus with and without reinforcement. Using data generated by a force sensor mounted on a reflex hammer in conjunction with the EMG sensor, you will make a calculation of nerve impulse speed.

OBJECTIVES

• Graph the electrical activity of a muscle activated by a reflex arc through nerves to and from the spinal cord

• Compare the relative speeds of voluntary and reflex muscle activation

• Associate muscle activity with involuntary activation

• Observe the effect of central nervous system influence on reflex amplitude

MATERIALS

• Computer or mobile device

• Data collection software

• EMG sensor with rectified signal capability

• Force sensor with reflex hammer attachment

• Electrode tabs

• Alcohol wipes

PROCEDURE

Select one person from your lab group to be the subject. Important: Do not attempt this experiment if you have pain in or around the knee. Inform your instructor of any possible health problems that might be exacerbated if you participate in this exercise.

Part I: Voluntary Activation of the Quadriceps Muscle

1. Attach the reflex hammer attachment to the force sensor according to the assembly instructions.

2. Connect and set up the sensors:

   1. Launch your data collection software

   2. Connect the EMG sensor to your computer or mobile device

   3. Configure the sensor to display the rectified EMG channel (rather than raw EKG)

   4. Connect the force sensor to your computer or mobile device

   5. Configure the force sensor coordinate system so that a press on the sensor hook generates positive force values

3. Set up the data-collection mode:

   1. Set sampling rate to 100 samples/s

   2. Set collection duration to 30 s

4. Have the subject sit comfortably in a chair that is high enough to allow his or her legs to dangle freely above the floor.

5. Attach two electrode tabs above one knee along the line of the quadriceps muscle between the knee and the hip (see Figure 3). The tabs should be 5 cm and 13 cm from the center of the patella. Place a third electrode tab on the lower leg.

6. Attach the red and green EMG leads to the electrode tabs above the knee with the red electrode closest to the knee. Attach the black lead (ground) to the electrode tab on the lower leg.

7. Start data collection to verify signal quality. If the graph has a stable baseline as shown in Figure 4, stop data collection and continue to Step 8. If your graph has an unstable baseline, stop and collect a new set of data. Repeat data collection until you have obtained a stable baseline for 5 s. Note: Previous data sets should be automatically saved each time. 

8. Collect voluntary muscle activation data. Note: Read the entire step before collecting data to become familiar with the procedure.

   • Have the subject close his or her eyes, or avert them from the screen

   • Start data collection

   • After recording 5 s of stable baseline, swing the reflex hammer briskly to contact the table or other surface that generates a sound

   • The subject should kick his or her leg out immediately upon hearing the sound

   • Continue obtaining reflexes (repeat the previous two steps) so that you record 5 to 10 kicks during the data-collection period

9. Determine the time elapsed between striking the table surface with the reflex hammer and the contraction of the quadriceps muscle:

   • On the force graph, select the first peak (which corresponds to the first kick). This peak indicates the time at which the table surface was struck. Record this time in Table 1.

   • On the EMG graph, select the first high peak (Kick 1). This peak indicates the time at which the quadriceps muscle contracted. Record this time in Table 1.

   • Repeat this process of determining the time of the hammer strike and reflex for a total of five stimulus-kick pairs

   • Calculate the change in time between the hammer strike and reflex for the five stimulus kick pairs and then calculate the average change in time for all five pairs. Record the values in Table 1.

Part II: Patellar Reflex

10. Locate the subject's patellar tendon by feeling for the narrow band of tissue that connects the lower aspect of the patella to the tibia. Use a pen to mark a horizontal line in the center of the tendon (see Figure 5). The tendon can be identified by its softness compared with the bones above and below.

11. Start data collection to verify signal quality. If your graph has a stable baseline as shown in Figure 4, stop and continue to Step 12. If your graph has an unstable baseline, stop and repeat data collection until you have obtained a stable baseline for 5 s.

12. Collect patellar reflex data. Note: Read the entire step before collecting data to familiarize yourself with the procedure.

    • Have the subject close his or her eyes or avert them from the screen

    • Start data collection

    • After recording 5 s of stable baseline, swing the reflex hammer briskly to contact the mark on the subject's tendon. If this does not result in a visible reflex, aim toward other areas of the tendon until the reflex is obtained.

    • Continue obtaining reflexes so that you record 5 to 10 reflexes during the collection period. If necessary, repeat this step until you have a data set with 5–10 reflexes.

13. Determine the time elapsed between striking the patellar tendon with the reflex hammer and the contraction of the quadriceps muscle:

    • On the force graph, select the first peak (which corresponds to the first kick). This peak indicates the time at which the tendon was struck. Record this time in Table 2.

    • On the EMG graph, select the first high peak (Kick 1). This peak indicates the time at which the quadriceps muscle contracted. Record this time in Table 2.

    • Repeat this process of determining the time of the hammer strike and reflex for a total of five stimulus-kick pairs

    • Calculate the change in time between the hammer strike and reflex for the five stimulus-kick pairs and then calculate the average change in time for all five pairs. Record the values in Table 2.

Part III: Reflex Reinforcement

14. With the subject sitting comfortably in a chair, start data collection. If your graph has a stable baseline, stop and continue to Step 15. If your graph has an unstable baseline, stop and repeat data collection until you have obtained a stable baseline for 5 s.

15. Collect patellar reflex data without and with reinforcement. Note: Read the entire step before collecting data to familiarize yourself with the procedure.

    • Have the subject close his or her eyes or avert them from the screen

    • Start data collection

    • After recording a stable baseline for 5 s, swing the reflex hammer briskly to contact the mark on the subject's tendon. If this does not result in a visible reflex, aim toward other areas of the tendon until the reflex is obtained.

    • After 5 or 6 successful reflexes have been obtained, have the subject reinforce the reflex by hooking together his/her flexed fingers and pulling apart at chest level, with elbows extending outward (see Figure 6).

    • Continue obtaining reflexes until data collection is completed at 30 s. A total of 10–15 reflexes should appear on the graph.

Figure 7

16. Determine the minimum, maximum, and Δy for the depolarization events:

    • Select the first area of increased amplitude (depolarization) on the EMG graph (see Figure 7)

    • Use your software's statistics or analysis tools to view the minimum and maximum values for this region

    • Record the minimum and maximum for this depolarization in Table 3, rounding to the nearest 0.01 mV

    • Determine and record the Δy value (amplitude) as the ΔmV

Show Image

17. Repeat this process for each of five unreinforced and five reinforced depolarization events, using the EMG graph to identify each primary reflex. Ignore rebound responses. Record the appropriate values in Table 3.

18. Determine the average amplitude of the reinforced and unreinforced depolarization events examined. Record these values in Table 3.

DATA TABLES