PDF(Pubmed)
Abstract：
Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.
摘要：
活动识别与人工智能相结合是一个重要的研究领域,跨越不同的领域，从体育和医疗保健到智能家居。在工业领域，和手动装配线，重点转移到人机交互，从而转移到复杂操作环境中的人类活动识别（HAR）。开发能够可靠有效地识别人类活动的模型和方法，传统上只归类为简单或复杂的活动，仍然是该领域的关键挑战。现有方法和途径的局限性包括它们不能考虑与所执行的活动相关联的上下文复杂性。我们应对这一挑战的方法是创建不同级别的活动抽象，这允许对活动有更细致的理解，并定义它们的潜在模式。具体来说,我们基于可在HAR中使用的已执行活动的上下文，为人类活动抽象级别提出了一种新的分层分类法。拟议的层次结构由五个级别组成，即原子，micro,meso,宏,还有mega.我们将此分类法与将活动分为简单和复杂类别以及其他类似分类方案的其他方法进行了比较，并在不同的应用程序中提供了实际示例以证明其有效性。关于人工智能等先进技术，我们的研究旨在指导和优化工业装配程序，特别是在不受控制的非实验室环境中，通过塑造工作流程来实现结构化数据分析，并在整个装配过程中突出显示各个级别的相关性。此外,它在研究人员和行业专业人士之间建立了有效的沟通和共同的理解，同时也为他们提供了促进系统开发的必要资源，传感器,和适应抽象级别的自定义工业用例的算法。
