Recent trends in educational technology focus on designing systems that can support students while learning complex psychomotor skills, such as those required when practicing sports and martial arts, dancing or playing a musical instrument. In this context, artificial intelligence can be key to personalize the development of these psychomotor skills by enabling the provision of effective feedback when the instructor is not present, or scaling up to a larger pool of students the feedback that an instructor would typically provide one-on-one. This paper presents the modeling of human motion gathered with inertial sensors aimed to offer a personalized support to students when learning complex psychomotor skills. In particular, when comparing learner data with those of an expert during the psychomotor learning process, artificial intelligence algorithms can allow to: (i) recognize specific motion learning units and (ii) assess learning performance in a motion unit. However, it seems that this field is still emerging, since when reviewed systematically, search results hardly included the motion modeling with artificial intelligence techniques of complex human activities measured with inertial sensors.

中文翻译：

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心理运动学习中的人工智能：从惯性传感器数据建模人体运动

教育技术的最新趋势集中在设计可以支持学生同时学习复杂的心理运动技能的系统，例如练习体育和武术、跳舞或演奏乐器时所需的那些。在这种情况下，人工智能可以成为个性化这些精神运动技能发展的关键，它可以在教师不在场时提供有效的反馈，或者将教师通常会提供的反馈扩大到更大的学生群体——在一个。本文介绍了使用惯性传感器收集的人体运动建模，旨在为学生学习复杂的心理运动技能提供个性化支持。特别是，在精神运动学习过程中将学习者数据与专家数据进行比较时，人工智能算法可以允许：（i）识别特定的运动学习单元和（ii）评估运动单元的学习性能。然而，该领域似乎仍在兴起，因为在系统审查时，搜索结果几乎不包括使用惯性传感器测量的复杂人类活动的人工智能技术的运动建模。