Evidence map›Paper›PMID 42485554›Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2026

Emerging Multimodal Artificial Sensory Fusion Paradigms Toward Advanced Embodied Intelligence.

Yang Guo, Yihua Huang, Xianyun Zhao, Huirun Chen, Huiqiao Li, Yuan Li, Tianyou Zhai

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yang GuoState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.
Yihua HuangState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.
Xianyun ZhaoState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.
Huirun ChenState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.
Huiqiao LiState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.ORCID https://orcid.org/0000-0001-8114-2542
Yuan LiState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.ORCID https://orcid.org/0000-0001-7452-1149
Tianyou ZhaiState Key Laboratory of New Textile Materials and Advanced Processing, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China.ORCID https://orcid.org/0000-0003-0985-4806

Funding

Guangdong Basic and Applied Basic Research Foundation 2023B1515120041Guangdong Basic and Applied Basic Research Foundation 2025A1515011072National Key R&D Program of China 2021YFA1200501National Natural Science Foundation of China 22535004National Natural Science Foundation of China 92580131National Natural Science Foundation of China U22A20137Natural Science Foundation of Hubei Province 2024AFE009Open Research Fund of Suzhou Laboratory SZLAB-1508-2024-ZD013Research Support Program of HUST 2025BRA006Research Support Program of HUST 2025ZDKJCX03Scientific Research Innovation Capability Support Project for Young Faculty SRICSPYF-ZY2025074Shenzhen Science and Technology Innovation Program CJGJZD20240729143104006Shenzhen Science and Technology Innovation Program JCYJ20240813153403005Special Zone Program of Wuhan Natural Science Foundation
6 · The paper itself

Abstract

In the endeavor of embodied intelligence to connect the digital world and physical reality, artificial multimodal sensory fusion has emerged as a cornerstone for endowing agents with robust perception and natural interaction capabilities. Specifically, multisensory fusion constructs a biomimetic sensing framework capable of parallel acquisition and processing of multi-source information. Such architectures augment information perception and ensure higher decision-making reliability within complex environments. However, a systematic review of this rapidly evolving field is noticeably absent. This review bridges this gap by providing a comprehensive analysis of various multimodal sensory fusion approaches. We introduce the configurations of multimodal sensory fusion, as well as the operating mechanisms of sensors and artificial synapses. In particular, various biomimetic multimodal fusion paradigms are critically analyzed, such as visual-tactile and visual-olfactory fusion, with detailed discussions of their device structure and fusion mechanisms. These multisensory fusion systems show great potential in various application fields. Finally, the emerging opportunities are discussed and outlined. By highlighting the critical potential of multimodal sensing fusion, this review aims to inspire further research on fusion strategies and practical applications, facilitating the evolution of embodied intelligence toward more human level.

Indexed as

Artificial IntelligenceBiomimeticsAnimalsHumansartificial synapseembodied intelligencemultimodal sensory fusionsensorsvisual‐tactile fusion

Identifiers

PMID42485554
PMCPMC13532493

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.