Ergonomic investigations on novel dynamic postural estimator using blaze pose and transfer learning

Abstract

The aim is to develop a computer-based assessment model for novel dynamic postural evaluation using RULA. The present study proposed a camera-based, three-dimensional (3D) dynamic human pose estimation model using ‘BlazePose’ with a data set of 50,000 action-level-based images. The model was investigated using the Deep Neural Network (DNN) and Transfer Learning (TL) approach. The model has been trained to evaluate the posture with high accuracy, precision, and recall for each output prediction class. The model can quickly analyse the ergonomics of dynamic posture online and offline with a promising accuracy of 94.12%. A novel dynamic postural estimator using blaze pose and transfer learning is proposed and assessed for accuracy. The model is subjected to a constant muscle loading factor and foot support score that could evaluate one person with good image clarity at a time.

Practitioner summary: A detailed investigation of dynamic work postures is largely missing in the literature. Experimental analysis has been performed using transfer learning, BlazePose, and RULA action levels. An overall accuracy of 94.12% is achieved for dynamic postural assessment.

Keywords:

• Dynamic postural assessment
• RULA
• transfer learning
• BlazePose
• human pose estimation
• deep neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Statement Data is available by personal communication with the author.

Code availability statement

The software code generated during the preparation of this manuscript is available with the corresponding author upon request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.