Energy expenditure estimation in boys with duchene muscular dystrophy using accelerometer and heart rate sensors

Amit Pande, Gretchen Casazza, Alina Nicorici, Edmund Seto, Sheridan Miyamoto, Matthew Lange, Ted Abresch, Prasant Mohapatra, Jay Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 languageEnglish (US)
Title of host publication2014 IEEE Healthcare Innovation Conference, HIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-29
Number of pages4
ISBN (Electronic)9781467363648
DOIs
StatePublished - Feb 10 2014
Externally publishedYes
Event2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States
Duration: Oct 8 2014Oct 10 2014

Other

Other2014 IEEE Healthcare Innovation Conference, HIC 2014
CountryUnited States
CitySeattle
Period10/8/1410/10/14

ASJC Scopus subject areas

  • Medicine(all)
  • Biomedical Engineering

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