BACKGROUND: Children with physical impairments are at a greater risk for obesity 
and decreased physical activity. A better understanding of physical activity 
pattern and energy expenditure (EE) would lead to a more targeted approach to 
intervention.
OBJECTIVE: This study focuses on studying the use of machine-learning algorithms 
for EE estimation in children with disabilities. A pilot study was conducted on 
children with Duchenne muscular dystrophy (DMD) to identify important factors 
for determining EE and develop a novel algorithm to accurately estimate EE from 
wearable sensor-collected data.
METHODS: There were 7 boys with DMD, 6 healthy control boys, and 22 control 
adults recruited. Data were collected using smartphone accelerometer and 
chest-worn heart rate sensors. The gold standard EE values were obtained from 
the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 
years) with DMD and controls. Data from this sensor setup were collected 
simultaneously during a series of concurrent activities. Linear regression and 
nonlinear machine-learning-based approaches were used to analyze the 
relationship between accelerometer and heart rate readings and COSMED values.
RESULTS: Existing calorimetry equations using linear regression and nonlinear 
machine-learning-based models, developed for healthy adults and young children, 
give low correlation to actual EE values in children with disabilities 
(14%-40%). The proposed model for boys with DMD uses ensemble machine learning 
techniques and gives a 91% correlation with actual measured EE values (root mean 
square error of 0.017).
CONCLUSIONS: Our results confirm that the methods developed to determine EE 
using accelerometer and heart rate sensor values in normal adults are not 
appropriate for children with disabilities and should not be used. A much more 
accurate model is obtained using machine-learning-based nonlinear regression 
specifically developed for this target population.