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Application of Artificial Intelligence Wearable Technology in the Big Data Analysis of Physical Activity in China
Author(s) -
Hao Cui,
Peng Zhi-qiang
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/1537389
Subject(s) - wearable computer , computer science , big data , wearable technology , energy consumption , field (mathematics) , data science , human–computer interaction , embedded system , data mining , electrical engineering , engineering , mathematics , pure mathematics
Although the validity of the physical activity questionnaire is low, the questionnaire is still the most commonly used measurement tool for physical activity research in China in the past 10 years. In the era of big data, research in the field of physical activity in China needs to be more effective, economical, convenient, and suitable for long-term, large-sample research tools. Acceleration detection technology, heart rate detection technology, and GPS technology are the mainstream technologies for measuring the energy consumption of physical activity in wearable devices. The application of data mining and machine learning methods further enhances the validity of the test. Domestic smart wearable devices are not effective in estimating energy consumption but still have a large space for technical improvement. Smart wearable devices have a very broad application prospect in the field of big data research in physical activity. The impact of smart wearable device technology and big data analysis methods on physical activity research will be far-reaching and may lead to major changes in research concepts, research tools, and data analysis methods.

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