Sensor-integrated industrial exoskeletons for ergonomic risk reduction: a systematic review of biomechanical outcomes and intelligent control systems

Introduction Industrial exoskeletons with integrated sensors are emerging as a technology for monitoring biomechanical exposure and supporting the prevention of work-related musculoskeletal disorders in industrial environments. Methods This systematic review followed the PRISMA 2020 guidelines, retrieving 106 studies published between 2021 and 2026 from the Scopus and Web of Science databases. VOSviewer version 1.6.20 and R‐Studio version 2026.01.2+418 were used for bibliometric and descriptive analyses. The review provides a qualitative synthesis supported by descriptive quantitative reporting of the extracted outcomes. Results The evidence indicated a technological shift from passive devices toward intelligent exoskeletons integrating surface electromyography, inertial measurement units, machine vision, physiological sensors, and artificial intelligence. Individual studies reported maximum reductions of 68.2% in muscle activity, 34% in spinal compression, and up to 60% in muscle fatigue, alongside productivity improvements of up to 59.5%. Machine learning approaches achieved an ROC of 0.985, automated assessment accuracies of 98.5%, and posture estimation with an ICC of 0.967. However, these values were not pooled estimates and varied across study designs, sensor configurations, tasks, and assessment protocols. Discussion The evidence remains constrained by small sample sizes, sensor drift, interoperability limitations, and insufficient long‐term validation in real‐world industrial environments. Sensor‐integrated exoskeletons show potential for ergonomic monitoring and risk reduction, but their effectiveness and industrial generalizability require further validation through standardized, interoperable, and longitudinal field studies.