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 | |
| 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 |
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ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
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
}
TY - GEN
T1 - A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer
AU - Wang, Qiao
AU - Lohit, Suhas
AU - Toledo, Meynard John
AU - Buman, Matthew
AU - Turaga, Pavan
PY - 2016/10/13
Y1 - 2016/10/13
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85009080470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009080470&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7591270
DO - 10.1109/EMBC.2016.7591270
M3 - Conference contribution
VL - 2016-October
SP - 2631
EP - 2635
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -