(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.

Human–robot interaction (HRI) takes place in dynamic environments where both humans and robots act as active agents, making the system inherently unpredictable. Abrupt movements can originate from either side and include human reflexes, fatigue, or unexpected reactions, as well as robot malfunctions, control errors, or task changes. These unpredictable events generate significant risks for both interaction fluency and safety, affecting not only the physical domain (e.g., collisions, excessive forces) but also cognitive aspects such as trust and predictability. Although different application areas present domain-specific challenges, a comprehensive overview of abrupt movements in HRI is still lacking, especially in the industrial scenario. This review aims to consolidate current knowledge regarding how abrupt phenomena are analyzed, prevented, and mitigated across various contexts and to offer new insights for researchers. In detail, after describing the literature search and the screening process, the review categorizes abrupt events, highlights key methodological approaches, and identifies gaps and future directions. By providing a structured synthesis of existing strategies, this work guides researchers in developing safer and more adaptive HRI frameworks capable of handling unpredictability.