Muscle fatigue as an investigative tool in motor control: A review with new insights on internal models and posture–movement coordination

Monjo, Florian; Terrier, Romain; Forestier, Nicolas

doi:10.1016/j.humov.2015.09.006

Cited by 36 publications

(29 citation statements)

References 100 publications

(107 reference statements)

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“…These results demonstrate forecasts which could be relevant for a wide range of applications. For example, as muscle fatigue develops over time, there are alterations in human motor control commands, compared to the normal (non-fatigued) state [61], resulting in a different biomechanical output. Anticipatory postural adjustments occur within tens of milliseconds and fatigue tends to affect these feed-forward mechanisms [62].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning

Moniri

Terracina

Rodríguez-Manzano

et al. 2021

IEEE Trans. Biomed. Eng.

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Objective: Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non-stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sMEG feature of the trunk muscles. Methods: Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for: up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises. Results: The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features. Conclusion: Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities. Significance: The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially * Ahmad Moniri and Dan Terracina contributed equally to this work.

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Section: Discussionmentioning

confidence: 99%

Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning

Moniri

Terracina

Rodríguez-Manzano

et al. 2021

IEEE Trans. Biomed. Eng.

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“…Muscle fatigability, i.e., a decline in the capacity to generate voluntary force, affects motor control 20 – 23 . A consequence of exercise-induced fatigability is a decrease in the dimensionality of the control 24 by increasing common input to several muscles 25 – 27 , which would imply impaired motor coordination during complex tasks 28 . Indeed, exercise-induced fatigability is likely to be related to changes in the excitability along the neural axis, which might increase the coupling of oscillatory neural inputs, reflecting in the common fluctuation in muscle activity across muscle pairs 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

Age-specific modulation of intermuscular beta coherence during gait before and after experimentally induced fatigue

Santos

Lamoth

Barbieri

et al. 2020

Sci Rep

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We examined the effects of age on intermuscular beta-band (15–35 Hz) coherence during treadmill walking before and after experimentally induced fatigue. Older (n = 12) and younger (n = 12) adults walked on a treadmill at 1.2 m/s for 3 min before and after repetitive sit-to-stand, rSTS, to induce muscle fatigability. We measured stride outcomes and coherence from 100 steps in the dominant leg for the synergistic (biceps femoris (BF)-semitendinosus, rectus femoris (RF)-vastus lateralis (VL), gastrocnemius lateralis (GL)-Soleus (SL), tibialis anterior (TA)-peroneus longus (PL)) and for the antagonistic (RF-BF and TA-GL) muscle pairs at late swing and early stance. Older vs. younger adults had 43–62% lower GL-SL, RF-VL coherence in swing and TA-PL and RF-VL coherence in stance. After rSTS, RF-BF coherence in late swing decreased by ~ 20% and TA-PL increased by 16% independent of age (p = 0.02). Also, GL-SL coherence decreased by ~ 23% and increased by ~ 23% in younger and older, respectively. Age affects the oscillatory coupling between synergistic muscle pairs, delivered presumably via corticospinal tracts, during treadmill walking. Muscle fatigability elicits age-specific changes in the common fluctuations in muscle activity, which could be interpreted as a compensation for muscle fatigability to maintain gait performance.

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“…Muscle fatigue has been defined as “the failure to maintain a required or expected force” [1] and it is a complex phenomenon experienced in everyday life that has reached great interest in the areas of sports, medicine and ergonomics [2]. Muscle fatigue can affect task performance, posture-movement coordination [3], position sense [4] and it can be a highly debilitating symptom in several pathologies [5]. For many patients with neuromuscular impairments, taking into account muscle fatigue is of crucial importance in the design of correct rehabilitation protocols [6] and fatigue assessment can provide crucial information about skeletal muscle function.…”

Section: Introductionmentioning

confidence: 99%

Muscle fatigue assessment during robot-mediated movements

Mugnosso

Marini

Holmes

et al. 2018

J NeuroEngineering Rehabil

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BackgroundSeveral neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.Methods40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects’ effort throughout the task, the energy consumption was estimated.ResultsFrom the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.ConclusionThe robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.

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Muscle fatigue as an investigative tool in motor control: A review with new insights on internal models and posture–movement coordination

Cited by 36 publications

References 100 publications

Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning

Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning

Age-specific modulation of intermuscular beta coherence during gait before and after experimentally induced fatigue

Muscle fatigue assessment during robot-mediated movements

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