Evidence map›Paper›PMID 42740056›Full record

ArticleSensors (Basel, Switzerland)2026

A Wearable Multimodal Assistive Interface for Virtual Cursor Control in Stroke Survivors with Upper-Limb Impairment.

Yuankai Liang, Liying Zhang, Ya Jiang, Junbiao Zhu, Yawen Zhao, Pengmin Qin, Di Chen, Junze Peng, Yuanqing Li, Xiquan Hu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

10 authors.

Yuankai LiangSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0009-0001-0671-9441
Liying ZhangDepartment of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, China.
Ya JiangSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Junbiao ZhuSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Yawen ZhaoDepartment of Rehabilitation Medicine, Guangzhou Dongsheng Hospital, Guangzhou 510500, China.
Pengmin QinResearch Center for Brain-Computer Interface, Guangdong Laboratory of Artificial Intelligence and Digital Economy (Guangzhou), Guangzhou 510335, China.ORCID 0000-0002-3569-6365
Di ChenSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Junze PengSchool of Computing and Data Science, The University of Hong Kong, Hong Kong 999077, China.
Yuanqing LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Xiquan HuDepartment of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, China.

Funding

National Natural Science Foundation of China 32371098STI 2030-Major Projects 2022ZD0208900the Guangzhou Talent Plan 2024D02J0008the Key R&D Program of Guangdong Province, China 2018B030339001the Key Realm Research and Development Program of Guangzhou, China 202007030007
6 · The paper itself

Abstract

Stroke survivors with upper-limb impairments often have difficulty using conventional computer interfaces, which limits their ability to perform daily computer-related activities independently. This study developed a wearable multimodal assistive interface that enables computer interaction through a virtual cursor. A lightweight headband equipped with electrooculography (EOG), electroencephalography (EEG), and an inertial measurement unit (IMU) was used to acquire multimodal signals for interaction control. EOG signals were processed to detect voluntary blinks to generate clicks, head movements were mapped to cursor movements through IMU-based control, and frontal EEG signals were used to estimate attention as an auxiliary mechanism for command verification. A rapid user-specific calibration procedure was introduced to adapt blink-detection thresholds to individual EOG characteristics without requiring extensive training. Thirty stroke patients with upper-limb impairments participated in experiments involving common computer tasks, including news reading, video playback, and character spelling. The system achieved an average operation accuracy of 87.53 ± 4.92%, an average operation time of 3.49 ± 0.49 s, and an information transfer rate of 62.04 ± 15.93 bits/min in the spelling task. The mean NASA-TLX score was 32.1 ± 5.4, indicating a moderate subjective workload during system use. These results demonstrate the feasibility of the proposed wearable multimodal assistive interface for supporting computer interaction in stroke survivors with upper-limb impairments.

Indexed as

StrokeStroke RehabilitationUpper ExtremityUser-Computer InterfaceWearable Electronic DevicesAdultAgedElectroencephalographyElectrooculographyFemaleHumansMaleMiddle AgedSurvivorsassistive technologyelectroencephalographyelectrooculographyhuman–computer interactioninertial measurement unitmultimodal interactionstroke rehabilitationvirtual cursorwearable assistive interface

Identifiers

PMID42740056
PMCPMC13568087

What OpenQuestion holds

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Registered trials

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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.