Enhancing instantaneous oxygen uptake estimation by non-linear model using cardio-pulmonary physiological and motion signals.

Wang, Zhao; Zhang, Qiang; Lan, Ke; Yang, Zhicheng; Gao, Xiaolin; Wu, Anshuo; Xin, Yi; Zhang, Zhengbo

Wang, Zhao; Zhang, Qiang; Lan, Ke; Yang, Zhicheng; Gao, Xiaolin; Wu, Anshuo; Xin, Yi; Zhang, Zhengbo.

Afiliação

Wang Z; Medical School of Chinese PLA, Beijing, China.
Zhang Q; School of Life Science, Beijing Institute of Technology, Beijing, China.
Lan K; Beijing SensEcho Science and Technology Co Ltd, Beijing, China.
Yang Z; PAII Inc., Palo Alto, Santa Clara, CA, United States.
Gao X; Institute of Sports Science, General Administration of Sport of China, Beijing, China.
Wu A; The Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States.
Xin Y; School of Life Science, Beijing Institute of Technology, Beijing, China.
Zhang Z; Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China.

Front Physiol ; 13: 897412, 2022.

Article em En | MEDLINE | ID: mdl-36105296

Palavras-chave

XGBoost; heart rate; machine learning; oxygen uptake; respiration; wearable sensor

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Physiol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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