AI-Powered Ergonomics: Enhancing Workplace Safety through Posture Detection

Balachandar Jeganathan
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Balachandar Jeganathan: Master of Science: Artificial Intelligence and Machine Learning Colorado State University Global, USA Master of Science: Computer Science, 05/2004 Annamalai University – India Bachelor of Science (Mathematics): Madurai Kamaraj University – India Database, and Data Analytics Certification: 12/2019 University of California Santa Cruz – Santa Clara, CA Author’s current affiliation: ASML, 80 W Tasman Dr, San Jose, CA 95134, USA

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 2, 410-429

Abstract: Traditional ergonomic assessments in workplaces are subjective, labor-intensive, and often inconsistent, limiting their ability to mitigate musculoskeletal disorders (MSDs). This study presents an AI-powered ergonomic framework integrating TensorFlow MoveNet for real-time posture detection and a Random Forest classifier for precise posture classification. Through a hypothesis-driven approach, we evaluate AI’s impact on workplace safety by analyzing real-world ergonomic risks and their mitigation using automated posture monitoring. Results indicate that the system achieved 100% accuracy in controlled testing and significantly improved workplace ergonomics in diverse settings. AI-driven posture correction reduced workplace injuries by 25% in manufacturing environments and office discomfort by 30%, demonstrating its effectiveness in reducing occupational health risks. Despite its potential, challenges such as data privacy, model generalization, and workplace adoption remain barriers to widespread AI-driven ergonomics adoption. This study highlights the importance of integrating AI into workplace safety standards, offering data-driven solutions to enhance employee well-being and operational efficiency.

Date: 2025
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