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240920s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202408478
|2 doi
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|a pubmed25n1258.xml
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|a (DE-627)NLM377871540
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|a (NLM)39300930
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Zhixin
|e verfasserin
|4 aut
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|a A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 15.11.2024
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|a Date Revised 15.11.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2024 Wiley‐VCH GmbH.
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|a 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
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|a Journal Article
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|a MG‐former model
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|a full‐process rehabilitation assessments
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| 650 |
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|a lower limb movement disorders
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| 650 |
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4 |
|a on‐skin sensors
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| 700 |
1 |
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|a He, Xinrun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bu, Tianzhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pang, Bo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tu, Zhongyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Zhiqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiao, Xiling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yin, Zhouping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Hao
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 46 vom: 19. Nov., Seite e2408478
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnas
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| 773 |
1 |
8 |
|g volume:36
|g year:2024
|g number:46
|g day:19
|g month:11
|g pages:e2408478
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202408478
|3 Volltext
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