Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound

Abstract

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes’ rehabilitation and strength training. However, traditional techniques rely heavily on the operator’s expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.