ロボット収集データでジェスチャー分類器を訓練する可能性の検討
Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems
スマートウォッチ装着時の人間の数字ジェスチャー認識において、人間の代わりにロボットアームで収集したデータで分類器を訓練できるかを検証した研究。
著者: Alejandro Garcia-Sosa, Jose J. Quintana-Hernandez, Miguel A. Ferrer Ballester, Cristina Carmona-Duarte
分類: cs.RO, cs.AI
原文アブストラクト
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.
