A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer

Qiao Wang; Suhas Lohit; Meynard John Toledo; Matthew Buman; Pavan Turaga

doi:10.1109/EMBC.2016.7591270

A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer

Qiao Wang, Suhas Lohit, Meynard John Toledo, Matthew Buman, Pavan Turaga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2631-2635
Number of pages	5
Volume	2016-October
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591270
State	Published - Oct 13 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016

Other

Other	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country	United States
City	Orlando
Period	8/16/16 → 8/20/16

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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Wang, Q., Lohit, S., Toledo, M. J., Buman, M., & Turaga, P. (2016). A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 2631-2635). [7591270] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591270

A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. / Wang, Qiao; Lohit, Suhas; Toledo, Meynard John; Buman, Matthew ; Turaga, Pavan.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 2631-2635 7591270.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, Q, Lohit, S, Toledo, MJ, Buman, M & Turaga, P 2016, A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. vol. 2016-October, 7591270, Institute of Electrical and Electronics Engineers Inc., pp. 2631-2635, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 8/16/16. https://doi.org/10.1109/EMBC.2016.7591270

Wang Q, Lohit S, Toledo MJ, Buman M , Turaga P. A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2631-2635. 7591270 https://doi.org/10.1109/EMBC.2016.7591270

Wang, Qiao ; Lohit, Suhas ; Toledo, Meynard John ; Buman, Matthew ; Turaga, Pavan. / A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2631-2635

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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.",

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AB - 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.

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