A Dataset of Standard and Abrupt Industrial Gestures Recorded Through MIMUs

Considering the human-centric approach promoted by Industry 5.0, safety becomes a crucial aspect in scenarios of human–robot interaction, especially when abrupt human movements occur due to inattention or unexpected circumstances. To this end, human motion tracking is necessary to promote a safe and efficient human–machine interaction. Literature datasets related to the industrial context generally contain controlled and repetitive gestures tracked with visual systems or magneto-inertial measurement units (MIMUs), without considering the occurrence of unexpected events that might cause operators’ abrupt movements. Accordingly, the aim of this paper is to present the dataset DASIG (Dataset of Standard and Abrupt Industrial Gestures) related to both standard typical industrial movements and abrupt movements registered through MIMUs. Sixty healthy working-age participants were asked to perform standard pick-and-place gestures interspersed with unexpected abrupt movements triggered by visual or acoustic alarms. The dataset contains MIMUs signals collected during the execution of the task, data related to the temporal generation of alarms, anthropometric data of all participants, and a script for demonstrating DASIG usability. All raw data are provided, and the collected dataset is suitable for several analyses related to the industrial context (gesture recognition, motion planning, ergonomics, safety, statistics, etc.).