A-smartphone-activity-monitor-that-accurately-estimates-energy-expenditure

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A-smartphone-activity-monitor-that-accurately-estimates-energy-expenditure

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# A smartphone activity monitor that accurately estimates energy expenditure

This repository contains the code and data for the paper "OpenMetabolics: Estimating energy expenditure using a smartphone worn in a pocket". The project involves training and validating a data-driven model for estimating energy expenditure using smartphone during physical activity.

## Repository Structure

The repository is organized into three main folders:

1. **Training data-driven energy expenditure estimation model**:
   - This folder contains code and data for training a data-driven energy expenditure model using cross-validation on a training dataset from a previous study.
   - Running `plot_feat_importance.py` will generate and save the feature importance ranking of the trained data-driven model (gradient-boosted trees).

2. **Visualization of real-world walking experiment results**:
   - This folder includes scripts for loading, processing, and visualizing data from real-world walking experiments using OpenMetabolics and other activity monitors including pedometer, heart rate model, and smartwatch.
   - The `validation_dataset` folder contains dataset of 28 participants.
   - For OpenMetabolics' estimation, the code performs gait segmentation by peak detection, discretizes segmented data, extracts statistical features, and estimates energy expenditure using a pre-trained data-driven estimation model.
   - The code generates raw energy expenditure estimate results from various methods and saves them in the `indiv_plot` folder.
   - The overall results of the real-world walking experiments are saved as a box plot in the `validation_dataset` folder.

3. **OpenMetabolics' pipeline code for a week-long monitoring study**:
   - This folder provides the complete pipeline code for OpenMetabolics, which estimates energy expenditure when a smartphone is carried in a pocket during a week-long monitoring test.

Each folder includes the necessary data, code, and results for validating OpenMetabolics. To run the code in each folder, execute `main.py`.

## Note
We will release the OpenMetabolics smartphone application on the app store and as open-source code upon publication.

## Installation

To run the code provided in this repository, ensure you have the following packages installed with the specified versions:

```bash
pip install matplotlib==3.10.0 numpy==2.2.3 pandas==1.3.5 scikit-learn==1.0.2 scipy==1.15.2 xgboost==1.7.6

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