中文版 | English
Title

Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers

Author
Corresponding AuthorGuan, Yu
Publication Years
2023-02-01
DOI
Source Title
ISSN
2524-521X
EISSN
2524-5228
Abstract
Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we develop an automated system that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data is collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we map the week-wise accelerometer data (3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we propose two types of new features, which can encode the rehabilitation information from both paralysed and non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities. Based on the proposed features, we further develop the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments are conducted to evaluate our system on both acute and chronic patients, and the promising results suggest its effectiveness.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China[12271239] ; Shenzhen Fundamental Research Program[20220111]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS Accession No
WOS:000928591100001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/502130
DepartmentDepartment of Statistics and Data Science
Affiliation
1.Hainan Rural Credit Union, Hainan, Peoples R China
2.Univ Warwick, Dept Comp Sci, Coventry, England
3.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen, Peoples R China
4.Nanjing Normal Univ, Sch Math Sci, Nanjing, Peoples R China
5.Newcastle Univ, Inst Neurosci, Newcastle Upon Tyne, England
Recommended Citation
GB/T 7714
Chen, Xi,Guan, Yu,Shi, Jian Qing,et al. Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers[J]. CCF Transactions on Pervasive Computing and Interaction,2023.
APA
Chen, Xi,Guan, Yu,Shi, Jian Qing,Du, Xiu-Li,&Eyre, Janet.(2023).Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers.CCF Transactions on Pervasive Computing and Interaction.
MLA
Chen, Xi,et al."Designing compact features for remote stroke rehabilitation monitoring using wearable accelerometers".CCF Transactions on Pervasive Computing and Interaction (2023).
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