2020 NASA International Space App Challenge

 
https://www.spaceappschallenge.org (Image taken from website)

https://www.spaceappschallenge.org (Image taken from website)

Challenge

“To develop an operational sleep shift scheduling tool that provides autonomous customization of a schedule for sleep, exercise, and nutrition to manage fatigue”

My Role

Product Management, Data Science, UI/UX Prototyping

My teammates

Aerospace Medicine - Research, Project Management

Software Engineering - App Development

Key Research Questions

  1. What are the variables that influence astronauts’ sleep?

  2. What kind of solution can we develop within the time constraint?

  3. Should we and/or which ones do we prioritize: sleep, exercise or nutrition?

Key Constraints

Time: 48 hours

Use of NASA datasets

Research:

Background search of primary research (e.g., academic papers, relevant videos/interviews/descriptions)

Review of relevant datasets

Define (Target Solution):

Focus on melatonin, as a variable that influences the circadian rhythm, to improve overall sleep quality.

Solution for Astronauts for use in Space.

 
 

Project & Product Management

Screen Shot 2021-09-09 at 12.31.52 AM.png
 

Ideate

Our solution considers three factors regarding sleep shifting:

1) Docking Time: Morning, Afternoon, Evening

2) Task vs. No Task

3) Landing Time: Morning, Afternoon, Evening

Constraints regarding scheduling:

  1. nap time has to occur either in morning or afternoon

  2. exercise occurs either in the morning or afternoon

  3. sleep time is fixed

  4. meal time is fixed

Critical Decisions:

Tool: App (Android)

Data:

  1. Nutrition Data (NASA)

  2. Dataset of Food & Beverages consisting of Melatonin

  3. Spacewalk Duration (NASA)

Screen Shot 2021-09-09 at 12.04.49 AM.png
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Low-Fidelity UI Wireframe in Figma

SpaceAppFinalUI-01.png

Prototype: Android App

See link below for demo

Architecture2.png
Preprocessing-02.png

Test and Limitations

Due to the time constraint, we did only an internal testing for main functions for demo purposes.

Limitations include not being able to test outside the team, no real-world user research (supplemented by research, watching secondary sources such as videos and interviews), not all functions were developed (machine learning for prioritizing food with Melatonin), dataset relevant to melatonin levels was not specific for astronauts.

 

Presentation and Results

The duration of the Challenge was during Mid-Autumn Festival!

The duration of the Challenge was during Mid-Autumn Festival!

Our team came in third for Taipei.

For full demo, code, and description:

https://2020.spaceappschallenge.org/challenges/sustain/sleep-shift-scheduling-tool/teams/solar-women/project