Artificial Intelligence and Wearable Devices in Bodybuilding: Effects of Adaptive Feedback on Movement Quality and Postural Compensation

Although bodybuilding is widely practiced, the integration of adaptive rule‑based wearable systems providing real‑time feedback for monitoring movement quality in bodybuilding‑specific training contexts remains limited. Although commercial systems for resistance‑training monitoring exist, their application to bodybuilding‑specific execution analysis remains underexplored. This study aims to evaluate the effectiveness of wearable devices providing real‑time adaptive feedback, particularly in their ability to monitor and enhance movement quality. A controlled experimental design was adopted, involving the collection and analysis of kinematic, angular, and temporal data within a structured training protocol. Variables were measured for each exercise under two conditions (pre‑ and post‑feedback). Results revealed aggregate mean improvements across exercises in execution symmetry (+12%), range of motion (+9%), and angular velocity (+11%), along with a reduction in technical errors (–18%), although the magnitude of change varied between movements. Improvements in time under tension (+5%) were observed but did not reach statistical significance. The findings demonstrate that real‑time adaptive feedback systems can effectively enhance movement quality in bodybuilding practice.