Predicting Sleep Quality From Daily Activity Logs Using Machine Learning

This is the final project work conducted for the Queens University ELEC 872 course Artifical Intelligence & Interactive Systems. This repository contains the project paper ELEC872_Project.pdf which includes a detailed summary of the project work.

The Python Notebook provided in this repository contains all experiments, data preprocessing, and data visulaizaiton, included within the paper "Predicting Sleep Quality From Daily Activity Logs Using Machine Learning".

Setup

General Setup

All required packages and versions can be found in the first few cell within the provided notebook 872_Project_Sleep_Quality.ipynb. The notebook and this repository should be cloned and executed using Google Colaboratory. More detailed instructions can be found in the notebook to complete the required setup steps.

Dataset

Dataset: Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D., Martini, C., & Morelli, D. (2020). Multilevel Monitoring of Activity and Sleep in Healthy People (version 1.0.0). PhysioNet. https://doi.org/10.13026/cerq-fc86.

The dataset from the citation above is included in the folder DataPaper. This data can also be obtained by visitng https://doi.org/10.13026/cerq-fc86 and downloding the zip file at the bottom of the page.