Evidence map›Paper›PMID 42062332›Full record

ArticleScientific reports2026

An intelligent HMM-based system for training brush techniques in ink painting.

Xinyue Hu, Yandan Ding, Hengyu Zhao, Yinyin Yu, Ziyi Xu, Kun Wu, Zhengsong Lin

Abstract read
In one paragraph

Article in Scientific reports, 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.

Xinyue HuSchool of Art and Design, Wuhan Institute of Technology, Wuhan, 430205, China.
Yandan DingSchool of Art and Design, Wuhan Institute of Technology, Wuhan, 430205, China.
Hengyu ZhaoSchool of Design, Nanjing University of the Arts, Nanjing, 210013, China.
Yinyin YuSchool of Art and Design, Wuhan Institute of Technology, Wuhan, 430205, China.
Ziyi XuSchool of Art and Design, Wuhan Institute of Technology, Wuhan, 430205, China.
Kun WuCollege of Information and Engineering, Taizhou University, Taizhou, 225300, China.
Zhengsong LinCollege of Art & Design, Nanjing Forestry University, Nanjing, 210037, China. lzs2023@njfu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present digital age, traditional paper-based practice for learning ink paintings often appears tedious for students in the new era, which increases the barrier to preserve the traditional art form of Oriental ink painting and to pass down its practice skills to future generations. This paper proposes an intelligent system shukong for coaching Oriental ink painting, which introduces a computer-aided 3D environment to coach brush manipulation in Oriental painting in real time. The system employs a Wii Remote as a digital paintbrush surrogate, capable of dynamically sensing user gestures for virtual brush manipulation. To explore the effectiveness of the new method, we conducted a user study of a modest scale, which involves two learner groups and five professional ink painters served as expert judges, and the comparative evaluation results demonstrate that the new method can more effectively help students acquire and improve their brush manipulation skills than using the conventional age-old practice. This digital method aims to strengthen students' muscle memory so that they can operate the brush more skillfully, and offers a new perspective for the preservation and transmission of Oriental ink painting as intangible cultural heritage.

Indexed as

Brush manipulationGesture recognitionLearning performance evaluationOriental ink painting

Identifiers

PMID42062332
PMCPMC13324027

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.