Using wearable activity type detection to improve physical activity energy expenditure estimation
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the Conference on Design, Automation and Test in Europe
Accurate energy expenditure estimation using smartphone sensors
Proceedings of the 4th Conference on Wireless Health
Proceedings of the 4th Conference on Wireless Health
Personalizing energy expenditure estimation using a cardiorespiratory fitness predicate
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Energy expenditure estimation with smartphone body sensors
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Hi-index | 0.00 |
Accurate estimation of Energy Expenditure (EE) in ambulatory settings is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. We present a new methodology for activity-specific EE algorithms. The proposed methodology models activity clusters using specific parameters that capture differences in EE within a cluster, and combines these models with Metabolic Equivalents (METs) derived from the compendium of physical activities. We designed a protocol consisting of a wide set of sedentary, household, lifestyle and gym activities, and developed a new activity-specific EE algorithm applying the proposed methodology. The algorithm uses accelerometer (ACC) and heart rate (HR) data acquired by a single monitoring device, together with anthropometric variables, to predict EE. Our model recognizes six clusters of activities independent of the subject in 52.6 hours of recordings from 19 participants. Increases in EE estimation accuracy ranged from 18 to 31% compared to state of the art single and multi-sensor activity-specific methods.