Experiment: Exploring Rates of Fatigue
Now that you've learned a bit about how your muscles fatigue, it's time to take advantage of this technology and knowledge to further your learning in a competitive way!
What will you learn?
In this experiment, you will learn more about muscle fatigue and compare the rate of fatigue between different sample groups.
- EMG's during Muscle Fatigue - You should read this experiment first to learn about how to use the Muscle SpikerBox and perform muscle fatigue experiments.
From the previous experiments we've learned how and why muscles fatigue, and we've learned how to view this process in real time using the Muscle SpikerBox, but we haven't yet begun to explore variation in the rate at which people's muscles fatigue. Age, gender, body-type, and lifestyle all play important roles in how your body's muscles have developed and, subsequently, how much effort they can exert before fatiguing.
Before diving into the muscle fatigue lab, let's learn a bit more about different kinds of muscle fatigue that you may encounter in life and science!
You can think of Central Fatigue as "mental" fatigue, also called CNS-fatigue (Central Nervous System). This type of fatigue is associated primarily with a reduction in neural drive or motivation. Central fatigue recordings are typically associated with progressively declining signal strength like a negative slope. Giving up on a movement could be a protective measure that the body employs to avoid sustaining any damage that could occur if you continued that sustained intensity. Central fatigue is something that can be largely "trained away," rather; it is something that affects novice athletes who are performing activities they do not have a lot of experience with. Distance running is an example of how central fatigue can differentiate a novice from a professional: a novice, who is not used to exerting themselves for such an extended period of time, is going to have a very difficult time running any significant distance and since they have not had a lot of training will likely stop well before their muscles would have given out - have you ever thought to yourself, "I could probably run much further, but I just don't waaannnnaaa?" An olympic distance runner, however, does not meet that same "mental" block and will continue to run through the discomfort.
This is the strictly physical variation of fatigue. Peripheral fatigue occurs when you simply cannot supply enough energy to the muscles to keep them active. If you remember back to the previous experiment where we learned about "orderly recruitment," a motor unit experiencing fatigue is replaced by a new muscle group. This is an example of the results of peripheral fatigue. When examining a recording of peripheral fatigue, it is common to see a decreasing trend, but with burst of renewed intensity at points within the recording. These are the moments when other motor units are jumping in to attempt to maintain the same amount of intensity. Peripheral fatigue is what you see when you're pumping iron at the gym or loading logs into your truck, or other short bursts of intense exercise.
Also called nervous fatigue. This is a much less frequently observed fatigue that is a result of neural fatigue, or nerves being unable to fire enough to keep up a muscular movement,. If a nerve cannot maintain a high intensity signal, it experiences "synaptic fatigue," the neural fatigue where a the nerve is no longer able to stimulate the motor unit it innervates. With exercise, this is only an issue at extreme levels of muscular activation and intensity. Similarly to Central Fatigue, Neuromuscular Fatigue can be addressed with adequate training over time. What makes this fatigue different is that it can also be a symptom of disease
Defining the Experiment
In this experiment we may be observing both primarily peripheral fatigue, maybe a bit of central fatigue, but we will not likely be observing neuromuscular fatigue. Take notes during your experiments and try to figure out what kind of fatigue you are witnessing!
The Slope, The Y-Intercept, and the Trend Line
Trend lines are used to investigate relationships between variables. By solving an equation for the line, we can represent our muscle fatigue signals with a trend line. Doing so will give us a value for the slope of the line, which we will be representative of the rate at which the strength of the signal decreases. We have to figure out what are our x and y variables for charting our results?? The variable we will be most concerned with is going to be called the "Rate of Fatigue" and will be represented by the (likely) negative slope of our trend line. We can use the RMS (Root Mean Squared, a positive measurement of the magnitude of a signal) as a measure of the intensity of the signal and then use measurements of RMS from the beginning and end of our recordings, over time, to calculate a trend line. This is our Rate of Fatigue, which will be our most reliable variable for this experiment as long as we follow this important condition: we will only use recordings that show a visible trend of decreasing signal power (as opposed to a recording where someone is clipping the recording's ceiling for 20 seconds and then completely relaxes). We're interested in measuring the change from the beginning of the signal's intensity, to the (presumably) lower intensity measured just before final relaxation, not how long someone can maintain full intensity before relaxing. When performing your experiments it is likely that you will see instances of both central and peripheral fatigue in action. For this experiment, we are going to create trend lines, which will measure the relationship between time and the intensity of the EMG signals, and then we will use that information to calculate the rate of fatigue for the two test groups.
But What to Test?
The first step of our experiment will be choosing your independent variable. For this experiment, you may be interested in focusing on different kinds of variables, maybe obviously objective ones such as comparing young and old subjects, boys and girls, or maybe you want to compare people who have a high-weight max bench press vs a low-weight max . For our example in this experiment we will be measuring the difference between three men and three women, all within a few years of the same age.
For our examples we will be recording using the Backyard Brains app.
Before you hook up a subject you have a couple of decisions to make: What muscle are you going to record from and what kind of activity are you going to have the subject perform? We'll be measuring from the forearm while having the subject attempt to grip an object for as long as possible, but you can choose your own methodology here when you perform the experiment yourself! Just keep in mind, we want to observe an isometric (stationary) example of muscle fatigue, like grip strength - so pick something that is not a dynamic movement (alternate example, have your subject arm wrestle with the wall, pushing their forearm and wrist against it as hard as they can, and measure from the forearm).
- First, connect your subject to the EMG SpikerBox and your EMG Spikerbox to your computer using a laptop cable. Begin by testing your signal: have your subject flex a few times and watch the computer to see the spikes.
- Next, we want to adjust the gain. Turn the wheel on your EMG all the way up, then have your subject flex as hard as they can. If the signal is clipping, turn down the "volume" wheel (gain of the signal) until you can see the tops and bottoms of all the spikes. Once you can, have your subject relax a couple of minutes. (a clipped signal looks like this)
- When your subject thinks they are ready, hit record on the app and immediately have them perform the procedure, keeping their muscle active for as long as possible. Remind them the focus isn't on maintaining max intensity for as long as possible before abruptly relaxing. Instruct the subject to shoot for about 70% strength and to keep the muscle active for as long as they can, even as they feel themselves getting "weaker."
- When they give out, or have noticeably plateaued at a low level, stop the recording. Your recording may look something like this:
- Write down the length of time of the recording, you'll need this later. Save the recording with identifiable notation, such as "Muscle Fatigue - Woman 1 - total time 118s."
Perform this with as many subjects as you can, the more values the more compelling the results! If you are in a classroom, get everyone involved! We'd recommend at least 10 people in each subject group, but if you are facing time constraints, you can do fewer.
Another quick note: Changing the gain on the spikerbox to adjust signals for clipping does affect the RMS read outs. Thus, the raw data, such as the beginning and ending strength, is not useful comparative data on its own. The slope calculation accounts for this because it is a measurement that is relative to the magnitude of the recording and, when averaged out, can then be used as a descriptive statistic for comparison. As long as you do not change the gain of the EMG Spikerbox DURING a recording, your data will be fine and your results will be workable.
Aggregating the Data
Our first step in interpreting our data is to turn our recordings into something a bit more quantifiable.
Open up your first recording, zoom all the way out; we're going to be measuring from the first and last five seconds of the recording:
With your right mouse button, click and drag from the beginning of the signal until you have selected a five second sample. While still holding down right click, look at the RMS value and write it down.
Look at the end of the recording. You should start your measurement at the point in the signal just before relaxation, dragging your selection to the left, and again selecting a five second sample and recording the RMS value.
For each recording you need these three variables: total time in seconds (Total Time(s)), RMS of the first five seconds (RMS Initial), and RMS of the last five seconds (RMS End). Repeat the process until you have collected this data for all your recordings.
Root Mean Square (RMS)
Let's take a quick look at RMS before we go any further, just so we understand what we're working with. RMS is not a measure of peak signal strangth, rather it is a measure of signal magnitude, which is calculated by squaring the signal, taking an average of the sum of squares, then taking the square root. Why do scientists do this though?
First, it's important to remember that our signal has a positive and negative bounds, like this:
So if we were to try to take an average of this signal, we wouldn't end up with much...
Remember from math class, how can you get rid of a negative? By squaring it! So let's try that with our signal:
That covers the square, but what about Root and Mean? Root and mean go hand in hand together here. Now we take the "sum of the squares" of the signal and divide it by the number of time intervals sampled (points) to get our mean (or average) value. Then you take the square root of your mean value to come up with the RMS!
Interpreting the Data
Now that we've got our values, let's chart them out to make them a bit easier to manage.
(Alpha, Beta, and Centauri, etc. are just the anonymous names for our subjects that we thought sounded cool...)
Now, we have a couple of calculations to make using our values...
Equation for a Line (and our trend line!):
This is the formula for our trend line, where m is equal to our slope (rate of fatigue), and where "b" represents where the line would intersect with the y-axis. But before we can solve for b, we need to solve for m.
"m" is the slope of our trend line that we're calculating for the fatiguing signals. It will also serve as our Rate of Fatigue variable, which we will use to compare our two test groups.
Since we're only working with two values right now, RMS and time, things will be a bit easier. Also, we have to take into consideration that our RMS measurements are themselves an average over five seconds. To account for this, divide the starting and total time in seconds by five. This effectively allows us to treat our measurements as if they were taken at a specific point, more easily allowing us to graph and math them. Keep in mind that your points are measurements over 5 seconds when presenting your results. Our tables end up looking like this:
With the starting time point at 1 and the end point at 17 (17 being the Total Time in Seconds / 5).
Time is our x variable and RMS is our y variable. We can solve for the rate of fatigue by plugging into the formula for the slope above
This is the value we are primarily concerned with, but let's go full circle here and practice calculating the trend line, or line of best fit. It will definitely be a useful skill in the future! Using our newly discovered m value, we can now solve for the equation of our line using the trend line formula from above!
Now that we have solved for the y-intercept of the trend line, we have all the information we need for the equation of the line!
This formula represents the trend line for the first test subject's EMG signal during the fatigue test. The last step, finding the y-intercept, isn't a necessary step for the information we are trying to gather for this particular experiment, but it is good to learn about the slope and line formulas together.
Record the slope, or rate of fatigue, of your first subject. Repeat the process above for your other recordings and add the information to your charts under a new column, RoF:
Let's try to understand our results. In our experiment, the women destroyed the men in terms of muscle endurance, coming in with a rate of fatigue that was almost exactly half of the men's! Why are the girls so much better at maintaining their strength over a long period of time? Are they stronger? not necessarily, endurance isn't really about strength. Are they tougher? Certainly. Are they just flat out better in terms of muscular endurance? Maybe! Lots of research has been done into looking at this difference (example) and it has been found (by us too!) that women's endurance is often greater than men's! Where can we see this in a non-scientific setting though? Women's impressive endurance gives them an edge in rock climbing - this is a sport where it is common to see women in direct competition with men. Research is also being done into women's endurance running and it looks like men and women are on equal footing there as well!
Use this example to guide your own experiments! Think about different variables you can test. You could also compare different muscles from the same body; investigate into whether people's arms or legs have different rates of fatigue, or just compare left and right arms. You can also make changes to the testing procedure, change the time frame of the data collection, etc. You can compare people at a neutral state, and then an impaired or excited state. What can you come up with? If you design an experiment and come up with some cool results, email us at firstname.lastname@example.org!and, with your permission, we'll share it on our website! You can contribute to the world's scientific knowledge!
Questions to Consider
- With our experiment, we left some variables to be taken care of by the "average," such as peripheral fatigue spikes in the recording. Can you come up with a way to get cleaner recordings?
- What can the trend lines teach you about muscle fatigue?
- Think of examples where you encounter muscle fatigue in your daily life. Does it ever serve as a beneficial function?
- If you start hitting the gym, pumping iron, and getting big, what will happen to your rate of fatigue?