Browsing by Author "Melanson, Edward L."

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    A Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors
    (Lippincott Williams & Wilkins, 2013) Dannecker, Kathryn L.; Sazonova, Nadezhda A.; Melanson, Edward L.; Sazonov, Edward S.; Browning, Raymond C.; Colorado State University; University of Alabama Tuscaloosa; University of Colorado Denver
    Introduction: Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. Purpose: This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. Methods: Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. Results: Estimated EE using the shoe-based device was not significantly different than measured EE (mean T SE; 476 T 20 vs 478 +/- 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively. Conclusions: The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.
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    Energy intake estimation from counts of chews and swallows
    (Elsevier, 2015) Fontana, Juan M.; Higgins, Janine A.; Schuckers, Stephanie C.; Bellisle, France; Pan, Zhaoxing; Melanson, Edward L.; Neuman, Michael R.; Sazonov, Edward; University of Alabama Tuscaloosa; University of Colorado Anschutz Medical Campus; Clarkson University; INRAE; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite Paris 13; heSam Universite; Conservatoire National Arts & Metiers (CNAM); Children's Hospital Colorado; Michigan Technological University
    Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, timeconsuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report. (C) 2014 Elsevier Ltd. All rights reserved.
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    Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform
    (IEEE, 2015) Sazonov, Edward; Hegde, Nagaraj; Browning, Raymond C.; Melanson, Edward L.; Sazonova, Nadezhda A.; University of Alabama Tuscaloosa; Colorado State University; University of Colorado Denver
    The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare the use of support vector machines (SVM), multinomial logistic discrimination (MLD), and multilayer perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a 4-h stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (similar to 95%) while reducing the running time and the memory requirements by a factor of >10(3). Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE = 0.78 kcal/min for SVM andMLD activity classification, 0.77 kcal/min for MLP versus RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE estimation on a wearable platform.