Human Activity Recognition in MMH: Pilot Study on Lifting and Lowering Classification
Year: 2025
Type: article
Abstract: Work-related musculoskeletal disorders are a leading cause of industrial injuries, with manual material handling (MMH) contributing to over 500,000 cases annually in the U.S. This study supports the development of a human digital twin (HDT) by enabling accurate human activity recognition for meaningful analysis. Using computer vision-based OpenPose for pose estimation, we classify two of fundamental MMH moves, lifting and lowering with Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. LSTM proved more robust, achieving 92.2% accuracy. This approach demonstrates the feasibility of non-invasive MMH monitoring, enabling real-time safety assessments and enhancing HDT-based industrial applications.
Source: IFAC-PapersOnLine
Authors: Shashwat Vyas, Chaitanya Vyas, Abhimanyu Sharotry, Jesus A. Jimenez, Apan Qasem, Francis A. Méndez Mediavilla
Institution: Texas State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414567681