This is the relationship between electromyography and thermography:
Thermography is a non-invasive evaluation and monitoring method that uses thermal cameras to measure the temperature distribution on the skin's surface to help analyze physical activity.
These temperature differences would allow, among other applications, to detect muscle activation patterns and asymmetries between body regions.
On the other hand, surface electromyography is a technique used to measure muscle activity during exercises or daily tasks.
This technique is mainly used to evaluate muscle synergies, and muscle activity, detect imbalances, and help the professional choose the most appropriate treatment, depending on the individual.
This methodology is proposed by the mDurance colleagues with whom we have reviewed the following article.
Recently the group of Arcangelo Merla et al. (Perpetuini et al. 2023) published an article with artificial intelligence for the prediction of the electromyographic response through thermography.
The authors have tried to relate muscle physiology and thermodynamics with their neural activity through the relationship between these two technologies.
The aim was to see if thermography had a relationship with muscle activity and could predict the point of fatigue. To do this, they instrumented the vast medial to measure with surface EMG during the realization of five series of squats until failure, with the aim of causing local fatigue. During the realization of the series, the temperature of 3 regions of interest very close to the electrodes was recorded through thermography.
The results show that there is a relationship between the amplitude of the EMG and the mean temperature of ROI 3 measured with thermography of (r=0.54; p<0.05).
In addition, the Gaussian prediction model to predict muscle activity estimated both variables with a high relationship (r=0.88; p<0.00), manifesting the ability of thermography to measure muscle activity.
From the ThermoHuman team, we describe the main conclusions of the study:
Perpetuini, D.; Formenti, D.; Cardone, D.; Trecroci, A.; Rossi, A.; Di Credico, A.; Merati, G.; Alberti, G.; Di Baldassarre, A.; Merla, A. Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue? Sensors 2023, 23, 832. https://doi.org/10.3390/s23020832