Frontiers in artificial intelligence
Meta-analysis of AI posture recognition for work-related musculoskeletal disorders prevention in occupational activities: comparison between untransformed, logit and Freeman-Tukey transformed performance
By Gorce P and Jacquier-Bret J.
Abstract
Introduction
The objective of this systematic review and meta-analysis was to evaluate the performance of artificial intelligence (AI) posture recognition systems in preventing the occurrence of work-related musculoskeletal disorders (WMSDs) across all occupational activities, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
Methods
During May 2026, six databases were screened without publication date restrictions. Selected studies had to evaluate the performance of a posture recognition system using a machine learning (ML) or deep learning (DL) approach to assess the risk level of WMSD development using at least one of the following metrics: accuracy, specificity, sensitivity, precision, and F1 score. Risk of bias, heterogeneity, sensitivity, publication bias, and certainty of evidence analyses were conducted in the meta-analysis. Logit and Freeman-Tukey transformations were applied to the data before pooling and results were compared to those obtained from the untransformed. A meta-analysis was performed by performance parameter and by transformation. Four subgroup analyses were performed: comparison of AI methods, sensor types, professional activities, and ergonomic tools used, using pairwise mixed-effects meta-regressions.