Understanding Your mTrigger Signal: Part 2 — Using the Signal to Guide Progression

What to watch during exercise, how to compare sessions, and when the signal may be misleading

Once you understand that a microvolt value is not a universal score, the next step is learning how to use the signal clinically. The real value comes from watching how muscle activity behaves as the patient repeats, progresses, fatigues, and moves.

What Should You Watch During an Exercise?

Rather than focusing on one peak number, look at the behavior of the signal.

Useful questions include:

• Does activation increase when the patient intentionally contracts the target muscle?
• Does the signal return toward baseline when the patient relaxes?
• Can the patient reach the target consistently across repetitions?
• Can they sustain the desired activation for the required amount of time?
• Does activation decrease or become less consistent as the patient fatigues?
• Does the target muscle remain active as the exercise becomes more functional or demanding?
• Is the patient achieving the desired signal while maintaining good movement quality and avoiding compensation?

This is where biofeedback becomes much more clinically useful than simply recording a maximum value.

For example, after knee surgery, a patient may initially struggle to generate and sustain a quadriceps contraction. Early success might mean simply seeing the signal rise reliably during each quad set. Later, the clinician may increase the activation target, introduce a straight-leg raise, and eventually progress into squats, step-downs, landing, or gait while ensuring that quadriceps activity remains present during progressively more demanding tasks.

That progression mirrors the way mTrigger is already being incorporated throughout rehabilitation rather than limiting biofeedback to isolated strengthening exercises.

What About Fatigue?

The TRACK feature can make it easy to notice that activation changes across a set. If a patient begins with strong, consistent contractions and then has difficulty reaching the same target, fatigue may be one possible explanation.

However, EMG and fatigue have a complicated relationship.

Changes in surface EMG amplitude during fatigue can reflect multiple physiologic processes, and research has reported increasing, decreasing, and relatively unchanged amplitudes depending on the muscle, task, contraction type, and stage of fatigue. A changing number should therefore prompt clinical interpretation rather than automatically being labeled “fatigue.” [1]

Use mTrigger alongside what you observe: Was the patient able to maintain the contraction? Did movement quality change? Did repetitions slow? Did compensation increase? Did the patient report fatigue?

The data is most powerful when combined with the rest of your clinical assessment.

Comparing One Session to the Next

Tracking progress is one of the most useful applications of mTrigger, but comparisons become more meaningful when you are comparing apples to apples.

Surface EMG amplitude can be affected by electrode placement, skin-electrode contact, body position, resistance, joint angle, movement speed, fatigue, and the task being performed. Standardization is therefore important when you want to compare activation over time. Research on EMG normalization similarly emphasizes maintaining consistent testing and reference conditions when comparing signals between trials or sessions. [2,3]

If you want to see whether a patient’s quadriceps activation is changing over several visits, try to reproduce the same exercise, position, resistance, electrode location, and instructions.

Then look at the trend.

Perhaps the patient can reach a higher activation goal. Maybe they can sustain it longer. Maybe they complete more successful repetitions before performance drops. Or perhaps they can now maintain the same quadriceps activation during a much more difficult functional task.

Those changes may be more meaningful than simply asking whether today’s peak microvolt value is higher than last week’s.

Know When the Signal Doesn’t Make Sense

Your clinical judgment still matters.

If the activation meter suddenly spikes when the patient isn’t contracting, jumps unpredictably during movement, remains unexpectedly low despite an obvious contraction, or doesn’t rise and fall with the intended muscle activity, check the setup before interpreting the results.

Movement of the leads, poor electrode contact, skin preparation, electrical interference, and worn electrodes can all affect signal quality. mTrigger’s existing troubleshooting and electrode-care resources address these issues in greater detail. [3]

A useful rule is simple:

The signal should make physiological sense.

If what you’re seeing on the screen doesn’t match what the patient is doing, investigate why before changing the exercise or drawing conclusions from the number.

Make the Number Meaningful

sEMG gives clinicians something traditional exercise cannot: the ability to make otherwise invisible muscle activity visible to the patient in real time.

But the value of biofeedback isn’t the microvolt number itself.

It is what you do with that information.

Establish a meaningful starting point. Give the patient an achievable target. Watch how the signal responds as they contract, relax, fatigue, and move.

Progress the task as control improves. Keep testing conditions consistent when you want to evaluate change over time. Most importantly, interpret muscle activity alongside movement quality, symptoms, strength, function, and the goals of rehabilitation.

Instead of asking:

“Is 800 μV good?”

Ask:

“Is the muscle doing what I want it to do during the movement that matters?”

That’s when the numbers on the mTrigger screen become useful clinical information.

Related mTrigger Resources

How to Use the TRACK Feature of mTrigger Biofeedback

Proper Electrode Habits

References

1. Dimitrova NA, Dimitrov GV. Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies. J Electromyogr Kinesiol. 2003;13(1):13-36. doi:10.1016/S1050-6411(02)00083-4.
2. Burden A. How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25 years of research. J Electromyogr Kinesiol. 2010;20(6):1023-1035. doi:10.1016/j.jelekin.2010.07.004.
3. Merletti R, Cerone GL. Tutorial. Surface EMG detection, conditioning, and pre-processing: Best practices. J Electromyogr Kinesiol. 2020;54:102440. doi:10.1016/j.jelekin.2020.102440.