Evidence map›Paper›PMID 41083557›Full record

ArticleScientific reports2025

Robotic arm control by augmented reality-assisted object detection.

Joung-Woo Hyung, Woochan Na, Kanghee Won, Dong-Joo Kim

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

4 authors.

Joung-Woo Hyung *Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea.
Woochan Na *Department of Information Display, Kyung Hee University, Seoul, 02447, Korea.
Kanghee WonDepartment of Information Display, Kyung Hee University, Seoul, 02447, Korea. khwon@khu.ac.kr.
Dong-Joo KimDepartment of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea.

Funding

National Research Foundation of Korea No. 2022R1A2C1013205National Research Foundation of Korea RS-2023-00280938
6 · The paper itself

Abstract

Various robotic arm control systems have been proposed to support the daily lives of patients with severe motor impairments; however, existing robotic arm control devices typically require physical input devices, such as joysticks, which are often difficult for patients with motor impairments to use. To overcome these limitations, this paper proposes a new method for controlling a robotic arm using augmented reality and object detection. The system automatically configures the path from the robotic arm to the object, allowing all operations to be performed using only eye tracking. With precise object localization and an intuitive gaze-based interface, the proposed robotic arm control system offers a significant advantage for patients with motor impairments, providing a more accessible and user-friendly alternative to traditional control methods.

Indexed as

ArmAugmented RealityRoboticsHumansAugmented realityObject detectionRobotic arm

Identifiers

PMID41083557
PMCPMC12518708

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.