title: Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors.
abstract: This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from different sensors. The EE estimation method designed in this way was evaluated on a dataset containing a wide range of activities. It was compared against three competing state-of-the-art approaches, including the BodyMedia Fit armband, the leading consumer EE estimation device. The results show that the proposed method outperforms the competition by up to 10.2 percentage points.
authors.forename: Bozidara
authors.initials: B
authors.lastname: Cvetkovic
authors.forename: Radoje
authors.initials: R
authors.lastname: Milic
authors.forename: Mitja
authors.initials: M
authors.lastname: Lustrek
mesh_terms: D000328:Adult; D001642:Bicycling / Q000502:physiology; D003020:Clothing; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D016138:Walking / Q000502:physiology; D055815:Young Adult