Recognition of Finger Movement Disability Level of Post-Stroke Patient Based on Fugl-Meyer Assessment Using Surface EMG

• Item type: SIG Technical Reports(1)
• 公開日: 2022-03-07
• タイトル: Recognition of Finger Movement Disability Level of Post-Stroke Patient Based on Fugl-Meyer Assessment Using Surface EMG
• タイトル / 言語: en
• タイトル: Recognition of Finger Movement Disability Level of Post-Stroke Patient Based on Fugl-Meyer Assessment Using Surface EMG
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 学習と支援
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Kobe University
• 著者所属: Kobe University
• 著者所属: Airlangga University-Dr. Soetomo General Hospital
• 著者所属: Kobe University
• 著者所属: Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Airlangga University-Dr. Soetomo General Hospital
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者名: Adhe, Rahmatullah Sugiharto; Shuhei, Tsuchida; I, Putu Alit Pawana; Tsutomu, Terada; Masahiko, Tsukamoto
• 著者名(英): Adhe, Rahmatullah Sugiharto; Shuhei, Tsuchida; I, Putu Alit Pawana; Tsutomu, Terada; Masahiko, Tsukamoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Regaining finger movement function is thought to be more challenging during rehabilitation because its muscle complexity. Thus, Fugl-Meyer Assessment (FMA) is employed by a doctor to evaluate manually the disability level of the finger movement. This will lead to subjectivity and risk of mistake during assessment. Thus, a system capable of predicting the disability level is required to aid the doctor in making more accurate judgement. This study aims to recognize the finger movement disability level based on Fugl-Meyer Assessment. The EMG recorded from 4 patients when they performed 7 movements based on FMA and extracting the time domain feature values. SVM and Random Forest were employed in classifying the disability level of each movement. SVM classifier could obtain better output in movement 4 which was 91.67% of accuracy and 0.78 of f1 score.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Regaining finger movement function is thought to be more challenging during rehabilitation because its muscle complexity. Thus, Fugl-Meyer Assessment (FMA) is employed by a doctor to evaluate manually the disability level of the finger movement. This will lead to subjectivity and risk of mistake during assessment. Thus, a system capable of predicting the disability level is required to aid the doctor in making more accurate judgement. This study aims to recognize the finger movement disability level based on Fugl-Meyer Assessment. The EMG recorded from 4 patients when they performed 7 movements based on FMA and extracting the time domain feature values. SVM and Random Forest were employed in classifying the disability level of each movement. SVM classifier could obtain better output in movement 4 which was 91.67% of accuracy and 0.78 of f1 score.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA1221543X
• 書誌情報: 研究報告ヒューマンコンピュータインタラクション（HCI）; 巻 2022-HCI-197, 号 10, p. 1-8, 発行日 2022-03-07
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8760
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会