  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.
