The objective of the present study was to understand the neuromotor strategies that slow down the development of fatigue in piano performance. Results showed that EMG activation variability increased more for the ShortDuration group with Time during both Digital and Chord tasks, while it did not change, or decreased for the LongDuration group. Additionally, a higher increased EMG activation variability of the biceps, anterior and lateral deltoids for the ShortDuration group were observed compared to the LongDuration group during the Digital task. In terms of kinematics, segment acceleration variability increased more with fatigue for the ShortDuration group at distal segments (i.e., right arm) during the Digital task, and at proximal segments (i.e., thorax and head) during the Chord task. Moreover, proximal joint angle (i.e., thorax lateroflexion) variability increased with time for the LongDuration group during the Digital task, while it did not change for the ShortDuration group. During the Chord task, distal joint angle (i.e., wrist flexion) variability was higher for the LongDuration group compared to the ShortDuration group.
4.1 Variability and time-to-task termination
Results of the present study highlight a higher EMG activation variability with time for the ShortDuration group (i.e., the fatigued group), which partially contradicts previous studies suggesting either a protective effect of EMG variability against fatigue development5, or a decreasing EMG variability with fatigue6. However, in tasks that require a high level of accuracy, such as piano performance, motor variability impacting performance variables is undesirable. This could explain why the non-fatigued pianists of the LongDuration group maintained a low-level of EMG activation variability, whereas the fatigued pianists of the ShortDuration group had an increasing EMG activation variability, possibly to impede further fatigue development5,23. The increased EMG activation variability was accompanied by a greater increase of the right arm acceleration variability during the Digital task, and a greater increase of the thorax and head acceleration variability during the Chord task for the ShortDuration group, as compared to the LongDuration group. This possibly explains the decreased control of key-attack velocity due to fatigue in Goubault et al., (2021). Interestingly, Madeleine et al., (2008) showed that experienced butchers had a larger variability than novices for several kinematic variables, but a reduced EMG variability compared to novices in simulated cutting tasks14. This suggests that higher EMG variability among novices could be seen as a lack of adaptation to the work task, as seen among computer workers24. In the present study, the two groups had similar levels of piano experience and practice, and only differed in the time to exhaustion and myoelectric manifestation of fatigue3. Nonetheless, pianists from the LongDuration group adopted specific motor programmes, with an increased thorax lateroflexion and wrist flexion variability during the Digital and Chord task, respectively, that could have reduced the level of activity of some of the upper-limb muscles, and could possibly explain the greater time-to-task termination. An increased thorax angle variability was previously shown in repetitive fatiguing tasks22,25,26 suggesting a possible adaptation strategy at proximal joints used to compensate for upper-limb fatigue or fatigue-related discomfort during repetitive dynamic tasks. As pianists’ movements are individualized27, trunk movement could be a compensatory mechanism that potentially reduces the risk of injury. Nevertheless, these results suggest that pianists of the ShortDuration group could be at higher risk of injury than pianists of the LongDuration group, since they presented similar increasing EMG activation variability and decreasing joint angle variability as in Madeleine et al., (2008). However, future studies are needed to verify this hypothesis.
4.2 Variability and fatigue
Results of previous studies suggest a task dependency in the evolution of EMG activation variability with fatigue. Indeed, an increasing EMG activation variability has previously been observed in arm muscles (i.e., upper trapezius, anterior deltoid, and biceps) during a repetitive fatiguing task28,29. In contrast, a decreasing EMG activation variability was observed with fatigue in biceps brachii and brachialis during isokinetic, concentric/eccentric elbow flexion6. In the present study, the time affected both ShortDuration and LongDuration groups during the Digital and Chord tasks. The EMG activation variability increased with time for some wrist and finger flexor and extensor muscles and for the triceps and superior trapezius during the Digital tasks. During the Chord task, a decreasing EMG activation variability was observed with time for some wrist and finger flexor and extensor muscles as well as for the biceps. The notion of task dependency is reinforced with kinematics adaptations, with an increasing acceleration variability with time for most segments during the Digital task, and a decreasing acceleration variability with time for the right forearm and hand during the Chord task. It is also reportable that acceleration variability of the thorax, for instance, was qualitatively four times higher during the Chord task compared to the Digital task, suggesting that pianists used more their trunk motion during the Chord task to produce keystrokes. To produce keystrokes, expert pianists may use multi-joint upper-limb movements30,31. These multi-joint movements can also involve trunk motion, which can contribute to creating hand and finger keystroke velocities32 as well as facilitating and initiating upper-limb movements33. The same observation can be made for other segments, i.e., acceleration variability was higher at the upper-limb(s) and the head during the Chord task compared to the Digital task. The different movement characteristics of both tasks3 could explain the differences observed in the evolution of segment acceleration variability. The Chord task requires movements of larger amplitude than the Digital task; fatigue could be at the origin of the decreasing acceleration variability of distal segments because of less movement amplitude or a negative impact on the musical intention with the development of fatigue. On the contrary, during the Digital task, distal segments’ movements being of smaller amplitude, segment acceleration variability increased and could be interpreted clearer as a “loss of control” due to fatigue. Overall, even if the acceleration variability evolved in either direction with fatigue during both Digital and Chord tasks, it may be interpreted as a consequence of fatigue development instead of a strategy to impede further fatigue progress.
4.3 Music and variability
High inter-trial repeatability of EMG measurements was observed in string players (violinists) with less than 10% deviation of the mean muscular activities from their averages over the repetitions34–36. Another study showed low variability in maximum mouthpiece forces and kinematics among trumpet players37. The highly repeatable muscle activation and kinematic patterns in musicians, reflected in the present study by marginal variability of expert pianists muscle activation and movement patterns, could be explained by the years of practice required to become an expert35,38. Ericsson et al., (1993) estimated that expert musicians spent over 10,000 hours of musical practice by the age of 2139, leading to reorganization in certain sensory and motor systems and their interface40. The present study highlights that expert pianists can reproduce muscle activity and movement patterns and partially use variability with the development of fatigue when performing repetitive fatiguing Digital and Chord tasks.
4.4 Limitations
Some limitations have been taken into consideration when interpreting the results. First, the maximal voluntary contraction was not performed for all muscles. The EMG normalization was therefore determined using the average of the 10 maximum amplitude values recorded during each task. However, this should have a limited impact on the intra-participant EMG activation variability results as each bipolar signal is compared to itself within task Initiation and Term./Mid. Then, angular joints measured using IMUs should be interpreted with caution since fusion algorithms used to estimate sensor orientations can be subject to error accumulation over time caused by environmental magnetic perturbations and drift41–43. Nonetheless, drift should have a limited impact on the interpretation of the results since joint angle variability was calculated over 15 cycles, representing approximately 30-seconds of data recording.
4.5 Conclusion
Overall, this study highlights a direct effect of time on the EMG activation and segment acceleration variability of pianists, whereas a higher joint angle variability was observed in pianists having greater time-to-task termination. This suggests a direct effect of fatigue on EMG activation and segment acceleration variability, while a protective effect of fatigue development could be attributed to joint angle variability. Also, expert pianists with lower time-to-task termination during standard repeated Digital and Chord tasks exhibited an increased EMG activation variability, a greater increased acceleration variability and a lower wrist and trunk angle variability with time as opposed to expert pianists having higher time-to-task termination. This highlights different neuromotor strategies between pianists of different endurance characteristics, that could have an impact on the prevalence of injury. Future research should verify this hypothesis by assessing the EMG activation and kinematic variability effects from a training intervention aimed at increasing pianists’ endurance.