Paper Info
Title
Estimation Of A Physical Activity Energy Expenditure With A Patch-Type Sensor Module Using Artificial Neural Network
Abstract
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.
Year
DOI
Venue
2021
10.1002/cpe.5455
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
energy expenditure, machine learning, neural network, patch-type sensor, training program
Journal
33
Issue
ISSN
Citations 
2
1532-0626
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Kyeung Ho Kang100.34
Si Ho Shin200.34
Jaehyo Jung321.60
Youn Tae Kim43811.62