Normalising electromyograms without maximal voluntary contractions

Human muscles speak a language of electricity. When we decide to move, our brains send signals down the spinal cord to motor neurons, which in turn fire electrical impulses into muscle fibers. This activity generates tiny voltage changes on the skin's surface, a signal known as electromyography, or EMG. Scientists have long used these signals to understand how we control our bodies, to assess nerve and muscle health, and to help design bionic limbs that respond to a user's intent. However, reading this electrical language is tricky. The size of the signal recorded on the skin depends on many things: how thick the skin is, how far the electrodes are from the muscle, and how the person is positioned. To compare muscle activity between different people or different days, researchers usually need to find a common reference point. Traditionally, this has meant asking a person to squeeze a muscle as hard as they possibly can, a maximum effort that serves as a "full scale" for the measurement.

This standard practice, while useful, comes with significant hurdles. Asking a person to exert their absolute maximum is not always possible. It can be painful for someone with an injury, exhausting for a patient recovering from illness, or simply too difficult for someone with a neurological condition that limits their ability to contract muscles fully. Furthermore, even for healthy people, the result of a maximum effort can vary from day to day depending on motivation or fatigue. For years, scientists have searched for a way to normalize these muscle signals without relying on that difficult, sometimes impossible, maximum effort. A team of researchers at Griffith University has now proposed a new method that looks at the shape of the electrical signal itself to determine its strength, potentially removing the need for a maximal squeeze entirely.

The researchers began with a simple observation about how muscle signals behave. When a muscle is barely active, the electrical signal is quiet and concentrated around a central point. As the muscle works harder and more fibers are recruited, the signal becomes louder and spreads out, becoming more varied in its intensity. The team hypothesized that this spreading, or the "width" of the signal's distribution, could act as a built-in ruler. Instead of asking a person to hit a maximum, they could simply measure how wide the signal gets during a task and use that width to estimate how hard the muscle is working relative to its maximum.

To test this idea, the team recorded muscle activity from seven healthy adults while they rode a stationary bicycle at various speeds and power levels, ranging from a gentle warm-up to an all-out sprint. They also included a few jumping tasks to broaden the range of muscle effort. For each session, they first calculated the traditional "maximum" by finding the strongest signal the person produced during any of the tasks. Then, they applied their new method to the same data. This new approach took the raw electrical signal, filtered out noise, and broke it into tiny, overlapping slices of time. In each slice, the researchers measured two things: how much the signal varied in strength and how wide the distribution of those strengths was. They used these two measurements to predict what the maximum effort would have been, effectively creating a normalized scale without ever asking the participant to give their all.

The results were strikingly close to the traditional method. When the researchers compared the new estimates against the standard maximum-effort measurements, the average difference was just over six percent. In other words, the new method predicted the intensity of the muscle effort with a high degree of accuracy, capturing the same peaks and valleys of activity as the conventional approach. The method worked consistently across the different cycling conditions, from low-power pedaling to maximal sprints. To see if this approach could work outside the controlled environment of a bike, the team tested it on a single independent participant performing walking, running, squatting, and jumping tasks. While the error rate was slightly higher in these more complex movements, averaging around twelve percent, the method still successfully captured the relative intensity of the muscle activity.

The study suggests that this distribution-based technique offers a practical alternative for situations where a maximum effort is not feasible. It could be particularly valuable for wearable devices used in the field, where setting up a complex calibration routine is impractical, or for clinical assessments involving patients who cannot safely perform a maximum contraction. The researchers noted that their method does not eliminate all sources of error, such as differences in electrode placement, but it removes the burden of finding a perfect maximum reference for every single measurement session. By relying on the natural statistical properties of the muscle signal itself, the team has demonstrated that it is possible to gauge muscle effort without demanding a maximal squeeze.

While the findings are promising, the authors are careful to frame this as a new option rather than a replacement for all existing methods. The validation was conducted primarily on healthy adults, and the team acknowledges that more work is needed to confirm if the approach holds true for people with neurological injuries or for different types of muscle contractions. The method also requires specific recording equipment and settings to ensure the signal quality is consistent. Nevertheless, the study provides a clear path forward for a simpler, less burdensome way to measure muscle activity. It suggests that the answer to "how hard is this muscle working?" might be hidden within the signal itself, waiting to be read without the need for a final, exhausting push.