Muscles are the engines of movement. By shortening or lengthening, they allow joints to rotate, and in the case of isometric (co)contractions, they restrict movement. When muscles contract, they generate an electrical signal. These can be detected with electrodes placed on the skin above them. This electrical muscle activity can be measured using electromyography (EMG).
In this way, we can gain insight into how muscles respond to different movements and loads. The advantage of EMG is that it has very high “temporal resolution.” This means that we can accurately capture the timing of muscle activity through this measurement method.
EMG also helps in identifying specific roles of different muscles in complex movements. In the case of walking, we can determine if the right muscles are performing the tasks they are intended for. For example, adductors are primarily meant to stabilize a leg, not to propel it forward, a task reserved for the hamstrings. However, if they do not function adequately, the stride length is usually increased, causing someone to “walk on the adductors,” leading to overuse (and an increased risk of groin injuries).
In short, EMG is a powerful tool in biomechanical research. However, it also has some limitations, especially when considering its application in everyday practice. EMG provides insight into electrical activity but does not offer direct information about the force generated by muscles. The size (amplitude) of a signal is not always related to strength.
Additionally, the placement of electrodes is crucial. A significant challenge is the so-called crosstalk, which is the contamination of signals. Because electrical signals radiate around, electrodes not only pick up activity from the specific muscle they are attached to but also signals from surrounding muscles. This can lead to issues in interpretation.
Finally, it is difficult, if not impossible, to measure the activity of muscles that are not close to the surface of the skin, such as the iliopsoas muscle. Since movement results from the interaction of numerous muscles, it is not easy to place this EMG data within a larger context of complex movements like walking. Furthermore, the interpretation of data requires a lot of expertise, as factors like muscle fiber type and layers of fat can influence the signals.
In addition to the fact that EMG systems are often bulky and expensive, data interpretation is challenging and time-consuming. Furthermore, it provides only limited insight into the context of overall movement, which makes the combination with other measurement techniques, such as the recording of kinematic data, necessary to truly gain a comprehensive understanding of motion.