title: A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer.
abstract: Energy expenditure (EE) estimation from accelerometer-based wearable sensors is important to generate accurate assessment of physical activity (PA) in individuals. Approaches hitherto have mainly focused on using accelerometer data and features extracted from these data to learn a regression model to predict EE directly. In this paper, we propose a novel framework for EE estimation based on statistical estimation theory. Given a test sequence of accelerometer data, the probability distribution on the PA classes is estimated by a classifier and these predictions are used to estimate EE. Experimental evaluation, performed on a large dataset of 152 subjects and 12 activity classes, demonstrates that EE can be estimated accurately using our framework.
authors.lastname: Qiao Wang
authors.forename: Suhas
authors.initials: S
authors.lastname: Lohit
authors.forename: Meynard John
authors.initials: MJ
authors.lastname: Toledo
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Buman
authors.forename: Pavan
authors.initials: P
authors.lastname: Turaga
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D004734:Energy Metabolism; D015444:Exercise; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D011336:Probability; D012016:Reference Values; D012044:Regression Analysis; D060388:Support Vector Machine; D014953:Wrist; D055815:Young Adult