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 languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2631-2635
Number of pages5
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

Fingerprint

Wrist
Accelerometers
Energy Metabolism
Probability distributions
Classifiers

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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 proceedingConference 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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