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Machine learning approach to gait deviation prediction based on isokinetic data acquired from biometric sensors.

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Title: Machine learning approach to gait deviation prediction based on isokinetic data acquired from biometric sensors.
Authors: Krechowicz, Adam1 a.krechowicz@tu.kielce.pl, Deniziak, Stanisław1 deniziak@tu.kielce.pl, Kaczmarski, Daniel1 d.kaczmarski@tu.kielce.pl
Source: Gait & Posture. Mar2023, Vol. 101, p55-59. 5p.
Database: Academic Search Ultimate
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Header DbId: asn
DbLabel: Academic Search Ultimate
An: 162438635
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Machine learning approach to gait deviation prediction based on isokinetic data acquired from biometric sensors.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=162438635
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.gaitpost.2023.01.015
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 5
        StartPage: 55
    Titles:
      – TitleFull: Machine learning approach to gait deviation prediction based on isokinetic data acquired from biometric sensors.
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            NameFull: Krechowicz, Adam
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            NameFull: Deniziak, Stanisław
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            NameFull: Kaczmarski, Daniel
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          Dates:
            – D: 01
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
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              Value: 09666362
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              Value: 101
          Titles:
            – TitleFull: Gait & Posture
              Type: main
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