System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels

TL;DR

This paper introduces a hierarchical taxonomy for human activity recognition in industrial settings to improve AI applications in manufacturing.

Contribution

A novel five-level hierarchical taxonomy for human activity abstraction is proposed to better capture contextual complexities in industrial environments.

Findings

The proposed taxonomy includes atomic, micro, meso, macro, and mega levels of activity abstraction.

Real-world examples demonstrate the taxonomy's effectiveness in industrial assembly procedures.

The approach enables structured data analysis and highlights correlations across abstraction levels.

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.

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