acceleep

Accelerometry-based Energy Expenditure Predection (using Deep Learning)

Thesis title: "Accelerometry-Based Prediction of Energy Expenditure in Preschoolers Using Deep Learning"

Associated R code for experimentation and model development.
The project is a valid R package, meaning you can devtools::install() it to load utility functions for analysis.

This repository is technically a valid R package to enable the documentation of helper functions, but will contain non-standard folders which are noted in .Rbuildignore to not interfere with the package building/installation process.

Primary Result / Final Models

The final models can be found in output/models.

Models are stored in HDF5 and using the following naming convention:

final-<network-type>-<Resolution>Hz-<accelerometer model>-<placement>-<outcome unit>-<timestamp>.hdf5

For example final-CNN-100Hz-actigraph-hip_left-Jrel-20201029123026.hdf5

Project Structure

• R and man: Utility functions for data preparation, analysis, model development, with their generated documentation.

• simulated-data: Simulation of structurally similiar accelerometry data, reshaping to keras/tensorflow-compatible shapes for experimentation. (The "dry run"-stage)

• data-cleaning: Code to read the raw .csv accelerometry and spirometry data, merge them, and save them as more space-efficient .rds files.

• modelling: Modelling code, i.e. keras models, output if possible, etc.

• holdout-validation, cross-validation, cross-validation-full: Code for final model evaluation runs.

• final-model-fit: Code to fit final models on all available data.

• output: Intermediate output for data description, including summary data.