Journal on Multimodal User Interfaces

Aims & Scope

The Journal of Multimodal User Interfaces publishes work in the design, implementation and evaluation of multimodal interfaces.

Research in the domain of multimodal interaction is by its very essence a multidisciplinary area involving several fields including signal processing, human-machine interaction, computer science, cognitive science and ergonomics.

This journal focuses on multimodal interfaces involving advanced modalities, several modalities and their fusion, user-centric design, usability and architectural considerations.

Use cases and descriptions of specific application areas are welcome including for example e-learning, assistance, serious games, affective and social computing, interaction with avatars and robots.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.1
2024	2.20

Journal Rank

Year	Value
2024	11158

Journal Citation Indicator

Year	Value
2024	224

SJR (SCImago Journal Rank)

Year	Value
2024	0.521

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	36

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• EmoNets: Multimodal deep learning approaches for emotion recognition in video

  Citation: 275

  Authors: Samira Ebrahimi, Xavier, Pascal, Caglar, Vincent, Kishore, Sébastien, Pierre, Yann, Nicolas, Raul, Mehdi, David, Aaron, Pascal, Roland, Christopher, Yoshua

• An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends

  Citation: 203

  Authors: Alexy, Shyamanta M.

• Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

  Citation: 192

  Authors: Loic, Ginevra, George

• Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

  Citation: 176

  Authors: Bo-Kyeong, Jihyeon, Suh-Yeon, Soo-Young

• A survey of assistive technologies and applications for blind users on mobile platforms: a review and foundation for research

  Citation: 139

  Authors: Ádám, György, Hunor, Tony

• Multimodal assistive technologies for depression diagnosis and monitoring

  Citation: 136

  Authors: Jyoti, Roland, Sharifa, Abhinav, Michael, Julien, Gordon, Michael

• When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry

  Citation: 130

  Authors: Laurel D., Philip C., Peter

• Model-based adaptive user interface based on context and user experience evaluation

  Citation: 112

  Authors: Jamil, Anees, Hafiz Syed, Rahman, Muhammad, Shujaat, Jaehun, Oresti, Sungyoung

• On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues

  Citation: 94

  Authors: Florian, Martin, Alex, Björn, Ellen, Roddy

• “Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design

  Citation: 88

  Authors: Katharina, Dominik, Ruben, Tobias, Elisabeth

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