Quantifying Quality of Actions Using Wearable Sensor

Abstract

This paper introduces a novel approach to quantify the quality of human actions. The presented approach uses expert action data to define the space in order to gauge the performance of any user to identify expertise level. The proposed approach uses pose estimation model to identify different body attributes (legs, shoulders, head..) status (left, right, bend, curl..), which is further passed to autoencoder to have a latent representation encoding all the relevant information. This encoded representation is further passed to OneClass SVM to estimate the boundaries based on latent representation of expert data. These learned boundaries are used to gauge the quality of any questioned user with respect to the selected expert. The proposed approach enables identifying any critical situations in real work environment to avoid risky positions.