Textile smart sensors based on a biomechanical and multi-layer perceptron hybrid method

Abstract

Resistance training is becoming increasingly important and widespread. Decomposition of the muscle loads applied is important for injury prevention and determining the load on the targeted muscles. In this study, a flexible textile PET (polyethylene terephthalate)/SP(Spandex) SWCNT (Single-walled carbon nanotube) stretch sensor was fabricated and attached at four locations: the elbow, brachioradialis/flexor carpi radialis, biceps brachii, and triceps brachii. The stretch sensors attached to the elbow can measure the angle of elbow flexion without an IMU 9-axis sensor using quadratic fitting. A Multi-Layer Perceptron (MLP) was used to decompose the muscle volume expansions of the 3muscle by angle. The model provided a good fit for all three muscles, with R-squared values ranging from Test set 0.98725 to 0.99815. Through one input and three ouput fitting, the muscle volume expansion quantities during the bicep barbell curl were decomposed and compared with data. The results showed that the brachioradialis/flexor carpi radialis muscle maintained 13% of the arm muscle volume up to 60°, then increased to 44% at 100°. The biceps brachii muscle steadily increased to 70% from 0° up to 60°, and then maintained 40% at 100° due to the volume increase of other muscles. The triceps brachii muscle maintained 9% of the arm muscle volume up to 90°, then increased to 20% at 100°. This study shows that muscle volume expansion can be easily measured with a non-body contact wearable device, unlike many existing contact methods for measuring muscle activity like EMG (electro-myography), etc. This study provides a novel approach for easily measuring muscle volume expansion and decomposition in wearable devices, which can indirectly indicate injury prevention and muscle loading in target areas through balance optimization among local muscles.