Evidence map›Paper›PMID 41593255›Full record

ArticleMedical & biological engineering & computing2026

A novel multi-modal signals dynamic assessment method of idiopathic scoliosis patients for rehabilitation.

Mingjie Dong, Chengyin Wang, Yinbo Chen, Yuechuan Zhang, Zhuosong Bai, Shuo Wang, Jianguo Zhang, Run Ji, Jianfeng Li, Bin Fang and 1 more

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Mingjie DongBeijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing, 100124, China.ORCID http://orcid.org/0000-0002-5303-3899
Chengyin WangBeijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing, 100124, China.
Yinbo ChenBeijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing, 100124, China.
Yuechuan ZhangDepartment of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Zhuosong BaiDepartment of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Shuo WangDepartment of Engineering Physics, Tsinghua University, Beijing, 100084, China.
Jianguo ZhangDepartment of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Run JiNational Research Center for Rehabilitation Technical Aids, Beijing, 100176, China.
Jianfeng LiBeijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing, 100124, China. lijianfeng@bjut.edu.cn.
Bin FangDepartment of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. fangbin1120@bupt.edu.cn.
Qianyu ZhuangDepartment of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China. zhuangqianyu@126.com.

Funding

Beijing Nova Program 20230484488key laboratories and engineering technology research centers in the field of rehabilitation under the Ministry of Civil Affairs No. 102118170090010009004National High Level Hospital Clinical Research Funding No. 2022-PUMCH-B-002Natural Science Foundation of Beijing Municipality No. L222012
6 · The paper itself

Abstract

Idiopathic scoliosis (IS) is a complex three-dimensional spinal deformity involving lateral curvature and vertebral rotation, significantly impacting patients’ physical and psychological well-being. Current rehabilitation assessment for IS predominantly relies on static X-rays and clinical experience, which overlooks crucial dynamic information during movement and fails to quantify the spine’s functional capacity. To address this limitation, we developed a dynamic assessment system that integrates multimodal signals. The system combines surface electromyography (sEMG) and spatial positioning information (SPI) to monitor, guide, and evaluate the rehabilitation motions of IS patients. We validated the system’s effectiveness through experiments on both healthy subjects and IS patients. The results demonstrate its potential to enhance data-driven, unsupervised rehabilitation for IS patients and to support future clinical applications in personalized rehabilitation planning.

Indexed as

ScoliosisSignal Processing, Computer-AssistedAdolescentElectromyographyFemaleHumansMaleDynamic assessmentIdiopathic scoliosisMulti-modal signalsRehabilitation motion

Identifiers

PMID41593255

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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.