Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Xu, Luyou; Zhang, Jinxi; Li, Zhen; Liu, Yu; Jia, Zhuang; Han, Xiaowei; Liu, Chenglin; Zhou, Zhixiong

Xu, Luyou; Zhang, Jinxi; Li, Zhen; Liu, Yu; Jia, Zhuang; Han, Xiaowei; Liu, Chenglin; Zhou, Zhixiong.

Affiliation

Xu L; Institute for Sport Performance and Health Promotion, Capital University of Physical Education and Sports, Beijing, China.
Zhang J; Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing, China.
Li Z; School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China.
Liu Y; Institute for Sport Performance and Health Promotion, Capital University of Physical Education and Sports, Beijing, China.
Jia Z; Institute for Sport Performance and Health Promotion, Capital University of Physical Education and Sports, Beijing, China.
Han X; Faculty of Education, Beijing Normal University, Beijing, China.
Liu C; Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing, China.
Zhou Z; Institute for Sport Performance and Health Promotion, Capital University of Physical Education and Sports, Beijing, China.

Front Physiol ; 14: 1202737, 2023.

Article de En | MEDLINE | ID: mdl-38028785

Mots clés

METs; artificial neural network; energy consumption; high accuracy; metabolic prediction model; physical activity; predicting energy expenditure; tri-axis accelerometer

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Front Physiol Année: 2023 Type de document: Article Pays d'affiliation: Chine Pays de publication: Suisse

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