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.

翻译：传感器与人工智能技术已经彻底改变了人体运动分析领域，但在训练智能系统时，特定样本的稀缺性构成了重大挑战，尤其是在神经退行性疾病诊断背景下。本研究探讨了利用机器人采集数据训练传统上依赖人类采集数据的分类系统的可行性。作为概念验证，我们使用ABB机械臂与Apple Watch记录了数字字符数据库，并比较了采用人类记录数据与机器人记录数据训练系统的分类性能。主要目标是确定能否将机器人运动数据作为训练素材，实现对佩戴智能手表的人类数字字符的准确识别。研究结果为使用机器人采集数据训练分类系统的可行性提供了重要见解，这项研究对需要可靠识别的多个领域具有广泛影响，尤其在人类特定数据获取受限的应用场景中。