Validation of accelerometer-based energy expenditure prediction models in structured and simulated free-living settings
Montoye, 2017
Description
Four acceleration-based artificial neural networks were developed for estimating energy expenditure in adults using an ActiGraph worn at one of four wear locations- the right hip, right ankle, or either wrist. Adults performed simulated activities of daily living, locomotion, and other exercises and the model was cross-validated using leave-one-out cross validation. This paper also developed identical algorithms using only structured or only simulated free-living data for training but they are not currently included in the repository.
The following models are available. Root Mean Square Error (RMSE) is also provided.
Artificial neural network for left wrist monitor (1.07 METs)
Artificial neural network for right wrist monitor (1.09 METs)
Artificial neural network for right ankle monitor (0.89 METs)
Artificial neural network for right hip monitor (1.02 METs)
Development/Validation
Sample: 24 (12 males) healthy adults, 18-80 years of age
Setting: Laboratory
Activities: Activities of daily living, cycling, walking and running, resistance exercise, stairs, stationary behaviors
Criterion: Oxycon Mobile (VO2)
Accelerometer(s): ActiGraph GT9X on right hip, ankle, and both wrists
Validation approach: Leave-one-out cross-validation
Instructions
The R code can be used to read in a csv file with the necessary features and then use the neural network to predict energy expenditure for each 30-s window. There is a separate file for each of the four models. There is an example data sheet for each of the monitor wear locations (ankle, hip, left and right wrist) with the variables described in the data dictionary that need to be present in the csv file. 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.
Note: Compared to the original source, code and sample data were edited for consistency, data dictionary was added, models were unchanged
Source Information
Reference:
Montoye, A. H., Conger, S. A., Connolly, C. P., Imboden, M. T., Nelson, M. B., Bock, J. M., & Kaminsky, L. A. (2017). Validation of accelerometer-based energy expenditure prediction models in structured and simulated free-living settings. Measurement in Physical Education and Exercise Science, 21(4), 223-234. https://doi.org/10.1080/1091367X.2017.1337638 Link to paper
Original source for code:
https://drive.google.com/file/d/0B-BgdTzyd2OxUDhwRWR6OTJwZmM/edit
Corresponding author: Alexander H.K. Montoye, montoyeah@alma.edu
Contact
Kimberly Clevenger at accelerometerrepository@gmail.com