Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine.

Kim, Jun-Hee; Kwon, Oh-Yun; Hwang, Ui-Jae; Jung, Sung-Hoon; Gwak, Gyeong-Tae

Kim, Jun-Hee; Kwon, Oh-Yun; Hwang, Ui-Jae; Jung, Sung-Hoon; Gwak, Gyeong-Tae.

Afiliação

Kim JH; Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
Kwon OY; Laboratory of Kinetic Ergocise Based on Movement Analysis, Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
Hwang UJ; Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
Jung SH; Department of Physical Therapy, Division of Health Science, Baekseok University, Cheonan, South Korea.
Gwak GT; Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.

Ergonomics ; : 1-10, 2023 Dec 01.

Article em En | MEDLINE | ID: mdl-38039103

Palavras-chave

Subacromial pain syndrome; machine learning; muscle strength; shoulder range of motion; support vector machine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Ergonomics Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Coréia do Sul

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