Detection of Muscle Activation during Resistance Training Using Infrared Thermal Imaging

Abstract

Infrared thermal imaging has been widely used to show the correlation between thermal characteristics of the body and muscle activation. This study aims to investigate a method using thermal imaging to visualize and differentiate target muscles during resistance training. Thermal images were acquired to monitor three target muscles (i.e., biceps brachii, triceps brachii, and deltoid muscle) in the brachium while varying the training weight, duration, and order of training. The acquired thermal images were segmented and converted to heat maps. By generating difference heat maps from pairs of heat maps during training, the target muscles were clearly visualized, with an average temperature difference of 0.86 °C. It was observed that training order had no significant effect on skin surface temperature. The difference heat maps were also used to train a convolutional neural network (CNN) to show the feasibility of target muscle classification, with an accuracy of 92.3%. This study demonstrated that infrared thermal imaging could be effectively utilized to locate and differentiate target muscle activation during resistance training.

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Utilization of deep learning to classify resistance training exercises by the fabricated resistive stretch sensor

“…Therefore, much research has been conducted to evaluate the relationship between muscle activation and muscle contraction. [6][7][8]14,15 One of the most common methods to gain information about muscle is measuring electromyography signal (EMG). Nevertheless, the EMG test requires either skin-adherent or needling electrodes to acquire data, which is not suitable for monitoring muscle activities during applications in training.…”

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