Wang, X., Zhang, J. orcid.org/0000-0001-9638-574X, Xie, S.Q. orcid.org/0000-0002-8082-9112 et al. (3 more authors) (2024) Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review. IEEE Sensors Journal. ISSN 1530-437X
Abstract
With the number of people surviving a stroke soaring, automated upper limb impairment assessment has been extensively investigated in the past decades since it lays the foundation for personalised precision rehabilitation. The recent advancement of sensor systems, such as high-precision and real-time data transmission, have made it possible to quantify the kinematic and physiological parameters of stroke patients. In this paper, we review the development of sensor-based upper limb quantitative impairment assessment, concentrating on the capable of comprehensively and accurately detecting motion parameters and measuring physiological indicators to achieve the objective and rapid quantification of the stroke severity. The paper discusses various features used by different sensors, detectable actions, their utilization techniques, and effects of sensor placement on system accuracy and stability. In addition, both the advantages and disadvantages of the model-based and model-free algorithms are also reviewed. Furthermore, challenges encompassing comprehensive assessment of medical scales, neurological deficits assessment, random movement detection, the effect of the sensor placement, and the effect of the number of sensors are also discussed.
Metadata
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Keywords: | Wearable sensors; Stroke assessment; Machine learning; Deep learning; Upper limb impairment | ||||||
Dates: |
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Institution: | The University of Leeds | ||||||
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) | ||||||
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Depositing User: | Symplectic Publications | ||||||
Date Deposited: | 09 Feb 2024 10:18 | ||||||
Last Modified: | 09 Feb 2024 16:48 | ||||||
Published Version: | https://ieeexplore.ieee.org/document/10422757 | ||||||
Status: | Published online | ||||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
Identification Number: | https://doi.org/10.1109/jsen.2024.3359811 |