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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals.

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Title: Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals.
Authors: Ling, Liuyi1,2,3, Lin, Zhu1, zhu_lin118@163.com, Feng, Bin2, Wei, Liyu2, Jin, Li2, Wang, Yiwen2
Source: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications; Apr2025, Vol. 66 Issue 2, p321-334, 14p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 184035918
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00051144.2025.2480423
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 321
    Titles:
      – TitleFull: Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals.
        Type: main
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          Name:
            NameFull: Ling, Liuyi
      – PersonEntity:
          Name:
            NameFull: Lin, Zhu
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            NameFull: Feng, Bin
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            NameFull: Wei, Liyu
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            NameFull: Jin, Li
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            NameFull: Wang, Yiwen
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          Dates:
            – D: 01
              M: 04
              Text: Apr2025
              Type: published
              Y: 2025
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              Value: 66
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            – TitleFull: Automatika: Journal for Control, Measurement, Electronics, Computing & Communications
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