Multimodal Perception-Driven Optimization of Mobile Interaction Interfaces and Supply Chain Efficiency.
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| Title: | Multimodal Perception-Driven Optimization of Mobile Interaction Interfaces and Supply Chain Efficiency. |
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| Authors: | Liu, Jia1 230069@zjtie.edu.cn, Gao, Tian2 andygao413@outlook.com |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 12, p140-153. 14p. |
| Subjects: | Cognitive load, Multimodal user interfaces, Sensory perception, Artificial neural networks, Deep reinforcement learning, Surrogate-based optimization, Distributed computing |
| Abstract: | In mobile supply chain operations, constraints imposed by mobile terminal resources, fluctuations in operator cognitive load, and inadequate interface adaptability severely limit operational efficiency and increase the risk of operational errors. To address these challenges, a mobile interaction interface optimization and efficiency enhancement model was proposed, grounded in cloud-edge collaborative multimodal perception. A lightweight temporal fusion network, integrating time-division cross-modal attention and knowledge distillation, was developed to enable efficient processing of multimodal data at the edge and accurate prediction of operator intent, thereby accommodating the resource constraints of mobile devices. A mechanism for estimating instantaneous cognitive load, driven by multimodal proxy indicators, was constructed. Combined with meta-reinforcement learning, a load-aware dynamic interface reconfiguration strategy was formulated, enabling real-time adaptation between the interface and operator state. A multimodal intent-driven Markov decision process-based workflow preloading model was established, and an online Bayesian optimization framework was incorporated to form a closed-loop optimization system, effectively improving supply chain efficiency. This study provides an innovative solution for the application of mobile interaction technologies in supply chain scenarios and promotes the deep integration of mobile multimodal interaction with industrial environments. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194824145 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multimodal Perception-Driven Optimization of Mobile Interaction Interfaces and Supply Chain Efficiency. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jia%22">Liu, Jia</searchLink><relatesTo>1</relatesTo><i> 230069@zjtie.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Tian%22">Gao, Tian</searchLink><relatesTo>2</relatesTo><i> andygao413@outlook.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 12, p140-153. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cognitive+load%22">Cognitive load</searchLink><br /><searchLink fieldCode="DE" term="%22Multimodal+user+interfaces%22">Multimodal user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+perception%22">Sensory perception</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In mobile supply chain operations, constraints imposed by mobile terminal resources, fluctuations in operator cognitive load, and inadequate interface adaptability severely limit operational efficiency and increase the risk of operational errors. To address these challenges, a mobile interaction interface optimization and efficiency enhancement model was proposed, grounded in cloud-edge collaborative multimodal perception. A lightweight temporal fusion network, integrating time-division cross-modal attention and knowledge distillation, was developed to enable efficient processing of multimodal data at the edge and accurate prediction of operator intent, thereby accommodating the resource constraints of mobile devices. A mechanism for estimating instantaneous cognitive load, driven by multimodal proxy indicators, was constructed. Combined with meta-reinforcement learning, a load-aware dynamic interface reconfiguration strategy was formulated, enabling real-time adaptation between the interface and operator state. A multimodal intent-driven Markov decision process-based workflow preloading model was established, and an online Bayesian optimization framework was incorporated to form a closed-loop optimization system, effectively improving supply chain efficiency. This study provides an innovative solution for the application of mobile interaction technologies in supply chain scenarios and promotes the deep integration of mobile multimodal interaction with industrial environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i12.62255 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 140 Subjects: – SubjectFull: Cognitive load Type: general – SubjectFull: Multimodal user interfaces Type: general – SubjectFull: Sensory perception Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Surrogate-based optimization Type: general – SubjectFull: Distributed computing Type: general Titles: – TitleFull: Multimodal Perception-Driven Optimization of Mobile Interaction Interfaces and Supply Chain Efficiency. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Jia – PersonEntity: Name: NameFull: Gao, Tian IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 12 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
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