An attention-based CNN for automatic whole-body postural assessment

Abstract

Fully automatic postural assessment is highly useful, but has been challenging. Conventional methods either require manual assessment by ergonomists or depend on special devices that are intrusive, thus being hardly feasible in daily activities and workplaces. In this work, an attention-based convolutional neural network (CNN) is developed for automatic whole-body postural assessment. The proposed network learns to identify highly relevant regions (or body parts) and extract features automatically. Risk of the posture is estimated from the extracted features accordingly. To evaluate the proposed method, a postural dataset, referred to as pH36M, is created by re-targeting Human3.6M, one of the largest publicly available datasets for pose estimation using the Rapid Entire Body Assessment (REBA) criteria. Experimental results on pH36M demonstrate that proposed method achieves promising performance in comparison to baselines and the average assessment scores are substantially aligned with human assessment with a Kappa value of 0.73.