Evidence map›Paper›PMID 41554716›Full record

ArticleNature communications2026

Wearable intelligent throat enables natural speech in stroke patients with dysarthria.

Chenyu Tang, Shuo Gao, Cong Li, Wentian Yi, Yuxuan Jin, Xiaoxue Zhai, Sixuan Lei, Hongbei Meng, Zibo Zhang, Muzi Xu and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
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

23 authors.

Chenyu Tang *Department of Engineering, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-6368-5639
Shuo Gao *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China. shuo_gao@buaa.edu.cn.ORCID http://orcid.org/0000-0003-3096-4700
Cong Li *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Wentian YiDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-4044-3063
Yuxuan JinCavendish Laboratory, University of Cambridge, Cambridge, UK.
Xiaoxue ZhaiDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
Sixuan LeiShenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Hongbei MengSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Zibo ZhangDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0009-0000-3685-9069
Muzi XuDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-6381-9863
Shengbo WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.ORCID http://orcid.org/0000-0003-1212-138X
Xuhang ChenDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0009-0003-1757-9303
Chenxi WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Hongyun YangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Ningli WangBeijing Tongren Hospital, Capital Medical University, Beijing, China.
Wenyu WangThrust of Smart Manufacturing, University of Science and Technology, Guangzhou, China.ORCID http://orcid.org/0000-0001-6580-8236
Jin CaoSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Xiaodong FengDepartment of Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Peter SmielewskiDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-5096-3938
Yu PanDepartment of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
Wenhui SongDepartment of Surgical Biotechnology, University College London, London, United Kingdom.ORCID http://orcid.org/0000-0001-8406-472X
Martin BirchallRoyal National Ear Nose and Throat and Eastman Dental Hospitals, University College London Hospital, London, UK.ORCID http://orcid.org/0000-0002-9211-1722
Luigi G OcchipintiDepartment of Engineering, University of Cambridge, Cambridge, UK. lgo23@cam.ac.uk.ORCID http://orcid.org/0000-0002-9067-2534

Funding

British Council 45371261National Natural Science Foundation of China (National Science Foundation of China) 62171014RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/K03099X/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/W024284/1
6 · The paper itself

Abstract

Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT's LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems.

Indexed as

DysarthriaPharynxSpeechStrokeStroke RehabilitationWearable Electronic DevicesFemaleHumansIntelligent SystemsLarge Language ModelsMaleMiddle AgedVibration

Identifiers

PMID41554716
PMCPMC12816716

What OpenQuestion holds

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LicenceCC BY
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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.