AI-Integrated AR as an Intelligent Companion for Industrial Workers: A Systematic Review
Augmented reality (AR) has gained significant attention in recent years for its applications in training and assistance in various industrial settings. Yet, a less understood question is: How can AR systems, coupled with artificial intelligence (AI) capabilities, adaptively tailor instructions and f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10795144/ |
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| Summary: | Augmented reality (AR) has gained significant attention in recent years for its applications in training and assistance in various industrial settings. Yet, a less understood question is: How can AR systems, coupled with artificial intelligence (AI) capabilities, adaptively tailor instructions and feedback interventions to the specific needs of users, their cognitive states, and levels of expertise during task execution? This paper addresses this question by conducting a systematic review that delves into three specific research areas: the state-of-the-art of AR-based systems for industrial applications in terms of features and training/assistance capabilities, the existing gaps in transforming AR into an “intelligent companion” that adapts to both the work context and the user’s needs, and how these sources of multimodal data captured by AR headsets, wearables, and IoT sensors can be harnessed to interpret, predict, and guide task performance and learning through AR. To this end, this paper synthesizes recent studies in the field of industrial AR, summarizing their main findings, contributions, and associated limitations when integrating AI capabilities into AR. The results suggest that AR can effectively tackle key industry challenges associated with training and upskilling, process improvement, and error prevention. However, limitations remain in integrating multimodal data-driven capabilities into AR to effectively tailor AR guides to how individual workers learn and perform complex industrial tasks. The paper concludes with a framework as well as several research directions and examples to realize intelligent AR systems enhanced with advanced AI capabilities for activity understanding, user modeling, and interventions, serving as adaptive and personalized companions for industrial workers. |
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| ISSN: | 2169-3536 |