Anna Gina Perri

Papers

Total Citations

101

H-Index

About

Anna Gina Perri is a leading researcher at the intersection of human-robot collaboration and industrial automation, with a primary focus on multi-modal sensing and human action recognition in manufacturing environments. Her most influential work centers on creating and analyzing datasets that enable safer, more intuitive human-robot interaction. Perri’s landmark contribution, the HA4M dataset (62 citations), provides a comprehensive multi-modal monitoring framework for human action recognition during complex assembly tasks, specifically the construction of an Epicyclic Gear Train by 41 subjects. This resource has become foundational for developing intelligent manufacturing systems. Complementing this, her performance analysis of the Microsoft Azure Kinect for body tracking (39 citations) directly addresses the critical need for real-time, reliable input data to control cooperative robots (cobots) and enhance worker safety. By rigorously evaluating sensor capabilities for cobot control, Perri’s work enables more responsive planning and safer reactions to unpredictable human movements. Her research is pivotal in bridging the gap between raw sensor data and practical, safety-critical industrial applications, making her a key figure in advancing the next generation of collaborative manufacturing.