Intention Prediction for Active Upper-Limb Exoskeletons in Industrial Applications: A Systematic Literature Review.

ABSTRACT: Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation pro

SUBMITTER: Hochreiter D 

PROVIDER: S-EPMC12431359 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature