A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor.

Lamooki, Saeb Ragani; Hajifar, Sahand; Kang, Jiyeon; Sun, Hongyue; Megahed, Fadel M; Cavuoto, Lora A

Lamooki, Saeb Ragani; Hajifar, Sahand; Kang, Jiyeon; Sun, Hongyue; Megahed, Fadel M; Cavuoto, Lora A.

Afiliação

Lamooki SR; Department of Mechanical Engineering, University at Buffalo, Buffalo, NY, 14260, USA. Electronic address: saebraga@buffalo.edu.
Hajifar S; Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA. Electronic address: sahandha@buffalo.edu.
Kang J; Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, 14260, USA. Electronic address: jiyeonk@buffalo.edu.
Sun H; Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA. Electronic address: hongyues@buffalo.edu.
Megahed FM; Farmer School of Business, Miami University, Oxford, OH, 45056, USA. Electronic address: fmegahed@miamioh.edu.
Cavuoto LA; Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA. Electronic address: loracavu@buffalo.edu.

Appl Ergon ; 102: 103732, 2022 Jul.

Article em En | MEDLINE | ID: mdl-35287084

Assuntos

Ciência de Dados; Dispositivos Eletrônicos Vestíveis; Ergonomia; Humanos; Fatores de Risco; Local de Trabalho

Palavras-chave

Change point detection; End-to-end learning; Ergonomic risk assessment; Human performance modeling; Wavelets; Wearable sensors

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dispositivos Eletrônicos Vestíveis / Ciência de Dados Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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