Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

• Main Authors: Kyeung Ho Kang, Mingu Kang, Siho Shin, Jaehyo Jung, Meina Li
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Electronics
• Subjects: energy expenditure; neural network; machine learning; patch-type sensor; steady state genetic algorithm; hybrid genetic-neural system
• Online Access: https://www.mdpi.com/2079-9292/10/7/861

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.