This repository contains various scripts and tools for analyzing a novel dataset of 21 elite female football athletes. The dataset comprises 17 days of actigraphy, well-being, caffeine consumption, screen time, and daily hand strength test data. The aim is to provide a comprehensive understanding of the interplay between lifestyle factors, sleep, and athletic performance.
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algorithms: The folder contains several sleep detection algorithms and non wear algorithms. Additional, it provides sleep statistic functions and a base class to load in the actigraphy files. -
data_preprocessing: Script for preprocessing and loading in of the original gt3x files. Addditional, a list of visualisations. -
generate_reports.py: Example script how to read in actigraphy files and generate sleep statistics, sleep annotations and plots. -
technical_validation.py: Script for performing technical validation of data or models. The script reproduces the figures and results of the paper. -
annonymisation.py: Script for anonymizing sensitive data. It applies techniques to remove or obfuscate personally which we used for the annonymisation of the data. -
transfer_learning_inference.py: Script for performing inference using transfer learning models. It applies a LSTM model which has been trained on the MESA sleep study to REST for sleep prediction.
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Clone the repository:
git clone https://github.com/simula/REST.git cd REST -
Create and activate a virtual environment:
conda env create -f environment.yml