Smartphone-based motion capture for gait quantification and symmetry analysis in moderate-stage stroke patients.

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Bibliographic Details
Title: Smartphone-based motion capture for gait quantification and symmetry analysis in moderate-stage stroke patients.
Authors: Zeng Y; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China; Department of Rehabilitation Medicine, Shenzhen University General Hospital, Shenzhen 518055, China., Peng Y; CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China., Xie L; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China., Liu L; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China., Fang P; CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China., Huang H; Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China., Chen S; Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510080, China., Qiu X; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China., Wei X; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China. Electronic address: weixj2016@smu.edu.cn., Li H; Department of Rehabilitation Medicine, Shenzhen Hospital, Southern Medical University, Shenzhen 518101, China; Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address: lihai2018@smu.edu.cn.
Source: Gait & posture [Gait Posture] 2026 Jul; Vol. 128, pp. 110192. Date of Electronic Publication: 2026 Apr 16.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Sciencem Country of Publication: England NLM ID: 9416830 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2219 (Electronic) Linking ISSN: 09666362 NLM ISO Abbreviation: Gait Posture Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2219
DOI:10.1016/j.gaitpost.2026.110192