Wang, Z., He, X., Bu, T. et al. (8 more authors) (2024) A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model. Advanced Materials, 36 (46). 2408478. ISSN 0935-9648
Abstract
Rehabilitation of patients with lower limb movement disorders is a gradual process, which requires full-process assessments to guide the implementation of rehabilitation plans. However, the current methods can only complete the assessment in one stage and lack objective and quantitative assessment strategies. Here, a full-process, fine-grained, and quantitative rehabilitation assessments platform (RAP) supported by on-skin sensors and a multi-task gait transformer (MG-former) model for patients with lower limb movement disorders is developed. The signal quality and sensitivity of on-skin sensor is improved by the synthesis of high-performance triboelectric material and structure design. The MG-former model can simultaneously perform multiple tasks including binary classification, multiclassification, and regression, corresponding to assessment of fall risk, walking ability, and rehabilitation progress, covering the whole rehabilitation cycle. The RAP can assess the walking ability of 23 hemiplegic patients, which has highly consistent results with the scores by the experienced physician. Furthermore, the MG-former model outputs fine-grained assessment results when performing regression task to track slight progress of patients that cannot be captured by conventional scales, facilitating adjustment of rehabilitation plans. This work provides an objective and quantitative platform, which is instructive for physicians and patients to implement effective strategy throughout the whole rehabilitation process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Wiley-VCH GmbH. This is the peer reviewed version of the following article: Wang, Z., He, X., Bu, T. et al. (8 more authors) (2024) A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model. Advanced Materials. 2408478. ISSN 0935-9648, which has been published in final form at https://doi.org/10.1002/adma.202408478. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. |
Keywords: | MG‐former model; full‐process rehabilitation assessments; lower limb movement disorders; on‐skin sensors |
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) |
Funding Information: | Funder Grant number Royal Society IEC\NSFC\211360 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Sep 2024 14:57 |
Last Modified: | 04 Dec 2024 14:37 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/adma.202408478 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217497 |
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