Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems
This research addresses data scarcity in gesture classification, particularly for diagnosing neurodegenerative diseases, but is incremental as it focuses on a proof-of-concept with numeric characters.
This study tackled the problem of scarce human movement data for training gesture classification systems by exploring robot-collected data as an alternative, finding it feasible for accurate identification of numeric characters with a smartwatch.
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.