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Keeping a sleep journal can be a chore, and creating actionable steps from historical data can be difficult and confusing. Doze is an intelligent sleep journal that uses machine learning to analyze user data and come up with conclusions about how specific habits affect contribute to or detract from the user's quality of sleep.

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Doze

Doze is an intelligent sleep journal that helps users identify daily habits that affect quality of sleep. Doze learns from historical data, and provides users with a percentage based prediction of whether or not the user will feel well rested the next day. Users can preview different scenarios of actions to understand how different activities affect them.

Users can also view historical entries and look into details of which actions they completed on specific days and see snapshots of Doze's predicted chance of feeling well rested for each historical entry. Users can also delete historical entries and sort by most recent and oldest entries.

In the trends section of the app, users can view which activities they completed when they were well rested and when they were not well rested, as well as see how many hours slept in recent entries, which days they felt well rested, how Doze's predictions are changing over time, and other metrics.

Wireframing and ERD

How to run Doze

  1. Navigate to the home of this repo and click on the code button. Then, select clone repo. Copy the URL.
  2. Open your terminal and enter git clone URL (paste the copied URL).
  3. Enter cd doze/api and enter json-server -p 8088 -w data.json.
  4. Enter .. and then npm start.

Technologies used

  • Brain.js
  • React
  • React Bootstrap
  • Javascript
  • Chart.js
  • Figma
  • Minor libraries (buttons and switches)

Big thanks

Cohort 40 and NSS teaching staff including Joe Shepherd, Bryan Nilsen, and Sage Klein.

About

Keeping a sleep journal can be a chore, and creating actionable steps from historical data can be difficult and confusing. Doze is an intelligent sleep journal that uses machine learning to analyze user data and come up with conclusions about how specific habits affect contribute to or detract from the user's quality of sleep.

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