Premium
Smart approaches for assessing free‐living energy expenditure following identification of types of physical activity
Author(s) -
Plasqui G.
Publication year - 2017
Publication title -
obesity reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.845
H-Index - 162
eISSN - 1467-789X
pISSN - 1467-7881
DOI - 10.1111/obr.12506
Subject(s) - energy expenditure , accelerometer , physical activity , wearable computer , computer science , identification (biology) , doubly labeled water , energy (signal processing) , wearable technology , activity recognition , variety (cybernetics) , artificial intelligence , physical medicine and rehabilitation , medicine , mathematics , statistics , biology , botany , embedded system , endocrinology , operating system
Summary Accurate assessment of physical activity and energy expenditure has been a research focus for many decades. A variety of wearable sensors have been developed to objectively capture physical activity patterns in daily life. These sensors have evolved from simple pedometers to tri‐axial accelerometers, and multi sensor devices measuring different physiological constructs. The current review focuses on how activity recognition may help to improve daily life energy expenditure assessment. A brief overview is given about how different sensors have evolved over time to pave the way for recognition of different activity types. Once the activity is recognized together with the intensity of the activity, an energetic value can be attributed. This concept can then be tested in daily life using the independent reference technique doubly labeled water. So far, many studies have been performed to accurately identify activity types, and some of those studies have also successfully translated this into energy expenditure estimates. Most of these studies have been performed under standardized conditions, and the true applicability in daily life has rarely been addressed. The results so far however are highly promising, and technological advancements together with newly developed algorithms based on physiological constructs will further expand this field of research.