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Clinical Trial
. 2018 Dec 18;13(12):e0208834.
doi: 10.1371/journal.pone.0208834. eCollection 2018.

Identification of muscle fatigue by tracking facial expressions

Marco C Uchida  1 , Renato Carvalho  2 , Vitor Daniel Tessutti  3 , Reury Frank Pereira Bacurau  4 , Hélio José Coelho-Júnior  1 , Luciane Portas Capelo  5 , Heloiza Prando Ramos  1   6 , Marcia Calixto Dos Santos  6 , Luís Felipe Milano Teixeira  1   7 , Paulo Henrique Marchetti  8
Affiliations
Clinical Trial

Identification of muscle fatigue by tracking facial expressions

Marco C Uchida et al. PLoS One. .

Abstract

Resistance training (RT) is performed at distinct levels of intensity from the beginning to the end of exercise sets, increasing the sensation of effort as the exercise progress to more vigorous levels, commonly leading to changes on the facial expression of RT practitioners. The objective of this study is to evaluate changes in facial expressions using the Facial Action Coding System(FACS) and the activation of facial muscles by surface electromyography(sEMG) at two different levels of effort during resistance exercise and to investigate the correlation between facial expression and exercise intensity and fatigue. Eleven healthy male participants [23±6years; 1.77±6 m; 78±10kg] performed a set of arm curl exercise at 50% and 85% 1RM until muscle fatigue. The Surface electromyography (sEMG activity was recorded simultaneously in areas of the epicranius muscle (EM) and zygomatic major muscle (ZM). Facial expression was recorded and blindly scored by five experienced examiners. Scores (0-5) were based on the level of activity of the ZM (lip corner puller-Action Unit 12-FACS) during exercise. Facial expression and sEMG data were obtained during the exercise at the first repetition and at muscle failure. The root mean square (RMS) of the sEMG amplitude of the EM was significantly increased between the first and last repetition (50%1RM:p = 0.002,d = 1.75; and 85%1RM:p = 0.002,d = 1.54). The RMS values for the ZM were significantly increased between the first and last repetition (50%1RM:p<0.001,d = 2.67; 85%1RM:p<0.001,d = 0.50). The RMS values for the ZM were also increased in 85%1RM compared to values obtained from 50%1RM (p = 0.001,d = 1.12) at the first repetition. AU12 scores and RMS values were not statistically different between 85%1RM and 50%1RM at the last repetition. Furthermore, there was a strong correlation (r = 0.61;p = 0.045) between AU12 scores and the sEMG peak for the ZM. In conclusion, changes in facial expression may be directly correlated with different resistance exercise intensities and fatigue.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Example of facial expression.
Facial expression at rest (A) using a score of zero and in the last exercise repetition until muscle fatigue (B) using a score of five. The oval dash corresponds to the AU12.
Fig 2
Fig 2. Representation of the execution of the arm curl exercise.
A: initial position; B: 90 degrees of elbow flexion, when the AU12 was analyzed and; C: final position.
Fig 3
Fig 3. Mean and standard deviation of the RMS peak for the epicranius and zygomatic major muscles during the first and last repetition.
*Significant difference between the repetitions (first and last); †Significant difference between the two levels of intensity, p<0.05.
Fig 4
Fig 4. Mean and standard deviation of the AU12 score in the first and last repetition.
*Significant difference between the repetitions; †Significant difference between the two levels of intensity, p<0.05.
See this image and copyright information in PMC

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