Abstract
Accurate Energy Expenditure (EE) Estimation is very important to monitor physical activity of healthy and disabled population. In this work, we examine the limitations of applying existing calorimetry equations and machine learning models based on sensor data collected from healthy adults to estimate EE in disabled population, particularly children with Duchene muscular dystrophy (DMD). We propose a new machine learning-based approach which provides more accurate EE estimation for boys living with DMD. Existing calorimetry equations obtain a correlation of 40% (93% relative error in linear regression) with COSMED indirect calorimeter readings, while the non-linear model derived for normal healthy adults (developed using machine learning) gave 37% correlation. The proposed model for boys with DMD give a 91% correlation with COSMED values (only 38% relative absolute error) and uses ensemble meta-classifier with Reduced Error Pruning Decision Trees methodology.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE Healthcare Innovation Conference, HIC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 26-29 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781467363648 |
| DOIs | |
| State | Published - Feb 10 2014 |
| Externally published | Yes |
| Event | 2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States Duration: Oct 8 2014 → Oct 10 2014 |
Other
| Other | 2014 IEEE Healthcare Innovation Conference, HIC 2014 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 10/8/14 → 10/10/14 |
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ASJC Scopus subject areas
- Medicine(all)
- Biomedical Engineering
Cite this
Energy expenditure estimation in boys with duchene muscular dystrophy using accelerometer and heart rate sensors. / Pande, Amit; Casazza, Gretchen; Nicorici, Alina; Seto, Edmund; Miyamoto, Sheridan; Lange, Matthew; Abresch, Ted; Mohapatra, Prasant; Han, Jay.
2014 IEEE Healthcare Innovation Conference, HIC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 26-29 7038866.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Energy expenditure estimation in boys with duchene muscular dystrophy using accelerometer and heart rate sensors
AU - Pande, Amit
AU - Casazza, Gretchen
AU - Nicorici, Alina
AU - Seto, Edmund
AU - Miyamoto, Sheridan
AU - Lange, Matthew
AU - Abresch, Ted
AU - Mohapatra, Prasant
AU - Han, Jay
PY - 2014/2/10
Y1 - 2014/2/10
N2 - Accurate Energy Expenditure (EE) Estimation is very important to monitor physical activity of healthy and disabled population. In this work, we examine the limitations of applying existing calorimetry equations and machine learning models based on sensor data collected from healthy adults to estimate EE in disabled population, particularly children with Duchene muscular dystrophy (DMD). We propose a new machine learning-based approach which provides more accurate EE estimation for boys living with DMD. Existing calorimetry equations obtain a correlation of 40% (93% relative error in linear regression) with COSMED indirect calorimeter readings, while the non-linear model derived for normal healthy adults (developed using machine learning) gave 37% correlation. The proposed model for boys with DMD give a 91% correlation with COSMED values (only 38% relative absolute error) and uses ensemble meta-classifier with Reduced Error Pruning Decision Trees methodology.
AB - Accurate Energy Expenditure (EE) Estimation is very important to monitor physical activity of healthy and disabled population. In this work, we examine the limitations of applying existing calorimetry equations and machine learning models based on sensor data collected from healthy adults to estimate EE in disabled population, particularly children with Duchene muscular dystrophy (DMD). We propose a new machine learning-based approach which provides more accurate EE estimation for boys living with DMD. Existing calorimetry equations obtain a correlation of 40% (93% relative error in linear regression) with COSMED indirect calorimeter readings, while the non-linear model derived for normal healthy adults (developed using machine learning) gave 37% correlation. The proposed model for boys with DMD give a 91% correlation with COSMED values (only 38% relative absolute error) and uses ensemble meta-classifier with Reduced Error Pruning Decision Trees methodology.
UR - http://www.scopus.com/inward/record.url?scp=84949923086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949923086&partnerID=8YFLogxK
U2 - 10.1109/HIC.2014.7038866
DO - 10.1109/HIC.2014.7038866
M3 - Conference contribution
AN - SCOPUS:84949923086
SP - 26
EP - 29
BT - 2014 IEEE Healthcare Innovation Conference, HIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -