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Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems

Authors :
Garcia-Sosa, Alejandro
Quintana-Hernandez, Jose J.
Ballester, Miguel A. Ferrer
Carmona-Duarte, Cristina
Source :
IGS2023, 2023, 116-120
Publication Year :
2024

Abstract

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.

Details

Database :
arXiv
Journal :
IGS2023, 2023, 116-120
Publication Type :
Report
Accession number :
edsarx.2405.04241
Document Type :
Working Paper