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

TL;DR

This paper explores using robot-collected movement data to train gesture classification systems, aiming to address data scarcity issues in human movement analysis, especially for diagnosing neurodegenerative diseases.

Contribution

It demonstrates the feasibility of using robot-recorded data to train classification systems traditionally reliant on human data, providing a new approach for data collection in movement analysis.

Findings

Robot-collected data can effectively train gesture classifiers.

Comparable performance between robot-trained and human-trained systems.

Potential to expand data collection in scenarios with limited human data.

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.