Validation of Kinebot

Occupational ergonomics has traditionally relied on manual and subjective assessment methods, limiting the standardization and scalability of workplace risk analysis. While tools such as RULA, REBA, and WEA are commonly used, these approaches face limitations in accuracy, reproducibility, and real-world applicability. Recent advancements in computer vision and artificial intelligence (AI) have opened new possibilities for estimating human movement with increased precision and speed, leading to more automated and data-driven ergonomic practices. This study evaluates the accuracy of the Kinebot platform—a solution based on Human Pose Estimation—by comparing its angular measurements with those obtained from the Vicon system, regarded as the gold standard in motion capture. Using simultaneous recordings of a motor protocol from both systems, data were processed and analyzed through Python scripts. The results demonstrate a strong correlation between the systems, confirming Kinebot as a reliable tool for ergonomic field assessments. This research thus contributes to the digital transformation of ergonomics, supporting a more precise, scalable, and AI-driven approach to the field.