"Which muscles do I have to use to pick my nose?" Fortunately, this is a question we don't have to ask ourselves. It is an automatic movement that seems quite simple to us. However, even everyday and automated tasks such as picking your nose, walking to the bus or reaching for a glass of water require the precise activation of the many muscles involved, which for most movements is a lot. Considering that the muscle represents a rather large scale, which can comprise over hundreds of motor units (each consisting of a motor neuron and the innervated muscle cells), the question of "how do I contract my muscle to pick my nose" becomes even more complicated. The challenge for the central nervous system is therefore to precisely time and scale the activation of our muscles to achieve movement goals.

Over half a century ago, the famous Russian neurophysiologist Bernstein proposed the idea that the central nervous system does not activate each muscle individually, but in synergies [1, 2]. Animal and human research has supported the theory of muscle synergy [3-5]. These consist of synergy vectors and the associated activation coefficient. The synergy vectors are pre-defined, weighted co-activations of several muscles that are spatially 'stored' in the spinal cord. To perform a task, supraspinal areas recruit these synergy vectors through descending temporal patterns called activation coefficients. This simplifies motor control, as only a limited number of synergy vectors need to be temporally scaled and activated, rather than each muscle individually [6].

To study muscle synergies, the activation of several muscles is recorded with surface electromyography electrodes. After processing the signals, computational factorization is used to extract the spatial synergy vectors and the temporal activation coefficients. Using this method, studies have shown that similar muscle synergies - called shared synergies - are used for different movements that involve similar subtasks. For example, shared synergies have been found in Nordic-walking and conventional walking [7] and various reaching tasks [9]. However, the majority of muscle synergy studies have investigated everyday tasks and automated behaviors. Findings from these studies may not necessarily be applicable to more complex tasks with lower levels of expertise. Furthermore, the analysis of muscle synergies in more complex movements could provide meaningful insights towards a better understanding of movement learning. The first issue in studying complex tasks is the problem of defining what a complex task is. As mentioned at the beginning, even everyday movements such as picking one's nose, reaching for a target, walking or running require the fine-tuned activation of many muscles and therefore present a complex challenge to the central nervous system. Thus, we can only speak of a scale from less complex to highly complex tasks, with complexity increasing with higher demands on memory processing, the number of execution possibilities involved, and one's own expertise in performing the task [10].

With this in mind, our study investigated muscle synergies during three different skateboard tricks - the ollie, the kickflip and the 360° flip (Figure 1). These movements can be defined as highly complex, because (1) years of dedicated training are required to perform them successfully, and even then not every attempt is successful; (2) the inclusion of the skateboard introduces additional movement execution possibilities; (3) participants had to fully concentrate on trick execution and were not able to multitask as during walking, indicating a high level of memory processing. In addition, the difficulty of the tricks increases from the ollie to the kickflip to the 360° flip, which is clearly reflected in the amount of practice skateboarders need to learn the tricks.

Figure 1: a: The Ollie is a basic jump with the skateboard without any rotation of the board. The board performs a 360° rotation around the x-axis (longitudinal axis) during a Kickflip, and a 360° rotation around the x- axis and the z-axis (vertical axis) during a 360°-flip; b – f: Ollie; g – l: Kickflip; m – r: 360°-flip; the analyzed time frames were between the lowest (b, h, n) and highest (e, k, q) point of a sacrum cluster marker.

In our study, the muscle activity of sixteen lower limb muscles (eight per leg) was recorded for six successful trails per trick of seven recreational skateboarders. Non-negative matrix factorization was then used to extract muscle synergies for each trick of each participant. Three to six synergies were recruited by the skateboarders to perform the tricks. Among other things, we evaluated the occurrence of shared synergies between tricks and the inter-participant variability.

Despite using slightly different methods, we consistently found that some, but not all, synergies were shared across the three tricks (Figure 2). This highlights that similar synergy vectors are recruited to perform tricks with different board rotations - simplifying the complexity for the central nervous system, as similar activation patterns can be used even for complex movements. However, the presence of additional trick-specific synergies raises the idea that during the learning process, new synergies are formed for specific subtasks. This formation of new synergy vectors could play an important role for the long time required to learn a new movement. Previous studies have shown that learning new synergy vectors takes much longer than learning to adapt the activation coefficients that temporally recruit existing vectors [11, 12].

Figure 2: Shared and task-specific synergies between every two tricks for one example participant; r = Pearson’s correlation coefficient; y-ticks indicate the muscle weight, x-ticks indicate the muscles for the front (f, muscle 1-8) and back (b, muscle 9-16) leg.

Upon closer inspection of the shared synergies, we found that all participants had one synergy present in all three tricks. This synergy consisted mainly of knee extensors and was activated at the beginning of the upward movement (Figure 3) - responsible for the basic and fundamental jumping task. This suggests that the central nervous system uses synergies of basic movements and only adds or adapts new synergies for subtasks that can't be achieved with pre-existing ones (e.g. turning the skateboard).

Figure 3: Synergy vectors and activation coefficients which were shared across all three tricks of all participants (P1…P7); y-ticks are the muscle weightings (left subplots) or level of activation (right subplots) for synergy vectors or activation coefficients. Each waveform indicates the average activation coefficient across trials per trick. The time interval between the lowest (0%) and highest (100%) point of a sacrum cluster marker was analyzed (% of observed movement). f = front leg; b = back leg.

Our inter-participant analyses revealed a high variability of synergies between participants, suggesting that individuals used different motor strategies to perform the tricks. As we did not use constraints such as foot placement, speed or jump height, the number of solution strategies to successfully perform the tricks was high, which may explain the inter-individual synergies. Interestingly, the temporal activation coefficients were more similar across participants than the synergy vectors, suggesting that while synergies need to be recruited at similar times, the actual muscle contributions within these synergies may be participant-dependent.

With our study, we have shown that highly complex movements provide comprehensive insights into human motor control and may explain certain learning mechanisms. Skateboarding was used as an example, and we strongly encourage further research on other highly complex movements beyond everyday tasks to extend our findings and shed more light on motor control.

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