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.

通过使用机器人收集的数据来训练分类系统，这项研究探究了在训练智能系统时特定样本的稀缺性所带来的挑战。研究结果在确定使用机器人运动数据作为训练数据来准确识别佩戴智能手表的人类数字字符的潜力方面提供了有价值的见解，对于那些访问人类特定数据有限的领域具有广泛的影响。