Human activity recognition (HAR) is fundamental in human-robot collaboration (HRC), enabling robots to respond to and dynamically adapt to human intentions. This paper introduces a HAR system combining a modular data glove equipped with Inertial Measurement Units and a vision-based tactile sensor to capture hand activities in contact with a robot. We tested our activity recognition approach under different conditions, including offline classification of segmented sequences, real-time classification under static conditions, and a realistic HRC scenario. The experimental results show a high accuracy for all the tasks, suggesting that multiple collaborative settings could benefit from this multi-modal approach.
@article{arxiv.2602.07024,
title = {A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration},
author = {Valerio Belcamino and Nhat Minh Dinh Le and Quan Khanh Luu and Alessandro Carfì and Van Anh Ho and Fulvio Mastrogiovanni},
journal= {arXiv preprint arXiv:2602.07024},
year = {2026}
}