A Convolutional Neural Network-based Framework for the Assessment of Human Muscles

Abstract

This paper presents a system for assessing men's physique, using advanced computer vision and deep learning methods. The pipeline involves the segmenting and gauging of the condition of various muscle groups. The proposed system is composed of two different deep learning models working in tandem, the first is an Instance Segmentation Mask R-CNN and the second is GoogLeNet. The Mask R-CNN is used for the accurate multi-class identification and detection of different muscle groups, such as chest, biceps, abs, and shoulders. GoogLeNet then further classifies each muscle group into various levels (level 1, level 2, up to level n). Furthermore, performance metrics, such as accuracy, precision, F1-measure and confusion matrices are used to evaluate the effectiveness of the proposed system. We believe, that as this system provides information regarding the physical shape/ form of a person, it can be used to augment Diet and Exercise Recommendation Systems (DERS) and can have many commercial as well as clinical applications.