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Lookup NU author(s): Dr Jian Shi, Professor Janet Eyre
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023, The Author(s).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.
Author(s): Chen X, Guan Y, Shi JQ, Du X-L, Eyre J
Publication type: Article
Publication status: Published
Journal: CCF Transactions on Pervasive Computing and Interaction
Year: 2023
Volume: 5
Pages: 206–225
Print publication date: 01/06/2023
Online publication date: 07/02/2023
Acceptance date: 21/12/2022
Date deposited: 23/02/2023
ISSN (print): 2524-521X
ISSN (electronic): 2524-5228
Publisher: Springer
URL: https://doi.org/10.1007/s42486-022-00124-3
DOI: 10.1007/s42486-022-00124-3
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