Artificial Neural Networks for Estimating Postural Risk Levels During Work Task Cycles

Abstract

Musculoskeletal disorders (MSDs) are a common occupational health concern caused by poor posture that reduces productivity. Traditional postural assessments rely on expert evaluation, which is time consuming and fails to capture the complexity of human movement, limiting its real–time applicability. This study proposes a data–driven approach to 3D–postural risk assessment using inertial measurement unit (IMU) sensors and eight Multilayer Perceptron Neural Network (MLPNN) models. A forecasting model is developed for real–time risk prediction. Sixty–nine participants performed repetitive tasks. Using MATLAB (Registered trademark.) scripts, the joint angle (JA) in all three planes was calculated for the upper-body segments. MLPNN models were developed for postural risk level classification using SPSS (Registered trademark.). The performance of the MLPNN models was evaluated using precision …