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利用人工智能辅助系统将运动学习原理应用于现实世界的运动任务中。

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

机构信息

Center for Applied Data Science (CfADS), Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, 33619 Bielefeld, Germany.

出版信息

Sensors (Basel). 2022 Mar 23;22(7):2481. doi: 10.3390/s22072481.

Abstract

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

摘要

人类可以自然而然地学习动作,但要达到运动技能的专家水平需要大量的时间和训练。在这篇综述中,我们展示了现代技术如何支持人们学习新的运动技能。首先,我们介绍了运动控制、运动学习和运动技能学习中的重要概念。我们还概述了机器学习算法和用于人体运动分析的传感器技术的快速扩展。将运动学习原则、机器学习算法和最近的传感器技术集成在一起,有可能为运动技能训练开发人工智能引导的辅助系统。我们对这些不同领域的整合提出了看法,以将实验室环境中的运动学习研究过渡到现实世界环境和现实世界的运动任务,并提出了一种逐步的方法来促进这种过渡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67d8/9002555/25706b52a6a4/sensors-22-02481-g003.jpg

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