Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network. (17th July 2019)

• Record Type: Journal Article
• Authors: Kang, Kyeung Ho; Shin, Si Ho; Jung, Jaehyo; Kim, Youn Tae
• Other Names: Ko Hoon guestEditor.; Marreiros Goreti guestEditor.; Cong Vinh Phan guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-17
• Subjects: energy expenditure -- machine learning -- neural network -- patch‐type sensor -- training program; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5455
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Chronic diseases such as coronary artery diseases and diabetes are caused by lack of physical activities and are leading causes of high death and morbidity rates. In particular, the imbalance of consumption energy and intake energy has increased adult diseases such as obesity with high mortality. Until recently, direct calorimetry by production calorie and indirect calorimetry by energy expenditure have been regarded as the best methods for estimating physical activity and energy expenditure. These calorimetry methods are associated with limited practicality such as data acquisition in a limited time, high cost, and wearing an inconvenient mask for oxygen uptake measurement. In this study, we propose the most accurate method using a wireless patch‐type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, we achieved RMSE of 0.1893 and R 2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. These results indicate that the proposed system is useful and reliable for monitoring user's energy expenditure when using attached patch‐type sensors workouts.