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

• URL: http://arxiv.org/abs/2405.04241v1
• Date: Tue, 7 May 2024 11:58:34 GMT
• Title: Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems
• Abstract summary: This study investigates the feasibility of utilizing robot-collected data to train classification systems. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data.
• Score: 40.34185280098731
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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