A holistic methodology for mitigating awkward postural risks: Evidence from South Indian small-scale industries.

BACKGROUND: Small-scale industries (SSI) are the global economy's backbone since most industrial workers are connected. Most of these workers are contractual and temporary without appropriate training. Also, the SSI does not have a standard workplace with an appropriate layout and infrastructure, as they manage with minimum resources. Therefore, the work hazards, i.e., musculoskeletal disorders and fatigue, often go unnoticed as holistic postural risk methodology is still scarce for identifying the awkward postures in SSI. OBJECTIVE: The present study proposes a novel holistic methodology to track and mitigate awkward postural risks in human-physical activities in SSI. To determine the effectiveness of the proposed methodology, a case study is presented in the South Indian Pump industry, wherein a critical workstation with a complex ergonomic work environment is employed. METHODS: An ergonomic evaluation was conducted empirically and numerically in the workplaces using Digital Human Models. In numerical evaluation, three virtual workspaces have been created to redesign the identified crucial workstation, focusing on ergonomics and workflow. RESULTS: The results obtained from the case study are encouraging for to use of the novel methodology in SSI. The case study reports that the proposed design significantly reduced the REBA score and WISHA lifting index by 6 and 1.20, respectively, without significant investment. CONCLUSION: The proposed methodology could encourage research to identify awkward posture in SSI.

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

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.