Weakly structured information aggregation for upper-body posture assessment using ConvNets

Abstract

Posture assessment aims to determine the risk associated with poor posture and thus avoid injury in subjects. Upper-body posture assessment from images offers an attractive alternative to manual methods by directly extracting relevant features for classification. A deep convolutional neural network is proposed to extract structured features from different body parts and learn shared features that are used to determine the appropriate assessment. The structured features are learned with triplet-based rank constraints based on head and torso separately. The shared feature and assessment function are learned with soft-max constraints based on posture risk measurements. Experimental evaluation on a self-collected upper-body posture dataset has verified the efficacy of the proposed method and network architecture.