Accelerometry-based prediction of energy expenditure in preschoolers
Steenbock, 2019
Description
Twenty-four models were developed for predicting energy expenditure in young children using accleration metrics from one of six monitor/wear location combinations (ActiGraph at each hip, GENEActiv at each wrist and the right hip, ActivPal on right thigh). Models were developed from children performing indoor and outdoor age-appropriate activities using leave-one-out cross-validation. Another 48 models are available for kJ/min and J/min/kg but are not included in this repository.
The following models are available. Root Mean Square Error (RMSE) is also provided.
Artificial neural network for ActiGraph on right hip (1.66 METs)
Linear model for ActiGraph on right hip (1.70 METs)
Mixed linear model for ActiGraph on right hip (1.70 METs)
Random forest for ActiGraph on right hip (1.56 METs)
Artificial neural network for ActiGraph on left hip (1.63 METs)
Linear model for ActiGraph on left hip (1.67 METs)
Mixed linear model for ActiGraph on left hip (1.67 METs)
Random forest for ActiGraph on left hip (1.52 METs)
Artificial neural network for GENEActiv on right hip (1.66 METs)
Linear model for GENEActiv on right hip (1.68 METs)
Mixed linear model for GENEActiv on right hip (1.69 METs)
Random forest for GENEActiv on right hip (1.53 METs)
Artificial neural network for GENEActiv on right wrist (1.62 METs)
Linear model for GENEActiv on right wrist (1.69 METs)
Mixed linear model for GENEActiv on right wrist (1.69 METs)
Random forest for GENEActiv on right wrist (1.48 METs)
Artificial neural network for GENEActiv on left wrist (1.61 METs)
Linear model for GENEActiv on left wrist (1.70 METs)
Mixed linear model for GENEActiv on left wrist (1.69 METs)
Random forest for GENEActiv on left wrist (1.47 METs)
Artificial neural network for ActivPal on right thigh (1.81 METs)
Linear model for ActivPal on right thigh (1.69 METs)
Mixed linear model for ActivPal on right thigh (1.69 METs)
Random forest for ActivPal on right thigh (1.56 METs)
Development/Validation
Sample: 41 (22 males) healthy children, 3.0-6.3 years of age
Setting: School
Activities: Free-choice, games, overground/self-paced walking and running, stationary
Criterion: MetaMax 3b (VO2)
Accelerometer(s): ActiGraph right hip, ActiGraph left hip, GENEActiv right hip, GENEActiv right wrist, GENEActiv left wrist, ActivPal right thigh (models are for one location/monitor so not all needed)
Validation approach: Leave-one-out cross-validation
Instructions
The R code can be used to read in a csv file with the necessary variables (as described in the data dictionary and seen in the sample data). The chosen model can then be used to predict energy expenditure. The authors also provide some code for extracting features. Further instructions are in the R code. More information about R is found here.
This code relies on R software which can be downloaded for free at https://www.r-project.org/
Attached Files
Download all files as a .zip or download individual files below.
Data conversion code (not edited/used by repository)
ActiGraph artificial neural network right hip
ActiGraph Linear model right hip
ActiGraph Mixed linear model right hip
ActiGraph Random forest for ActiGraph right hip
ActiGraph Artificial neural network for ActiGraph left hip
ActiGraph Linear model for ActiGraph left hip
ActiGraph Mixed linear model for ActiGraph left hip
ActiGraph Random forest for ActiGraph left hip
GENEActiv Artificial neural network for GENEActiv right hip
GENEActiv Linear model for GENEActiv right hip
GENEActiv Mixed linear model for GENEActiv right hip
GENEActiv Random forest for GENEActiv right hip
GENEActiv Artificial neural network for GENEActiv right wrist
GENEActiv Linear model for GENEActiv right wrist
GENEActiv Mixed linear model for GENEActiv right wrist
GENEActiv Random forest for GENEActiv right wrist
GENEActiv Artificial neural network for GENEActiv left wrist
GENEActiv Linear model for GENEActiv left wrist
GENEActiv Mixed linear model for GENEActiv left wrist
GENEActiv Random forest for GENEActiv left wrist
ActivPal Artificial neural network for ActivPal right thigh
ActivPal Linear model for ActivPal right thigh
ActivPal Mixed linear model for ActivPal right thigh
ActivPal Random forest for ActivPal right thigh
Note: Compared to the original source, code was edited for consistency, data dictionary was added, models were unchanged
Source Information
Reference:
Steenbock, B., Wright, M. N., Wirsik, N., & Brandes, M. (2019). Accelerometry-based prediction of energy expenditure in preschoolers. Journal for the Measurement of Physical Behaviour, 2(2), 94-102. https://doi.org/10.1123/jmpb.2018-0032 Link to article
Original source for code:
https://github.com/bips-hb/EE_prediction
Corresponding author: Berit Steenbock, steenbock@leibniz-bips.de
Contact
Kimberly Clevenger at accelerometerrepository@gmail.com