Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited.
@article{arxiv.2405.04241,
title = {Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems},
author = {Alejandro Garcia-Sosa and Jose J. Quintana-Hernandez and Miguel A. Ferrer Ballester and Cristina Carmona-Duarte},
journal= {arXiv preprint arXiv:2405.04241},
year = {2024}
}