Project Cozie - A smartwatch methodology for quick and easy experience surveys

What is Cozie?

Cozie is a Fitbit Ionic, Versa, Versa Lite and Versa 2 clock face that can ask people questions. It is useful for experience sampling research and was designed for the built environment, although there are also forks focused on Covid-19 symptoms tracking

Please cite us if you use this software:

Jayathissa, Prageeth, Matias Quintana, Tapeesh Sood, Negin Nazarian, and Clayton Miller. 2019. “Is Your Clock-Face Cozie? A Smartwatch Methodology for the in-Situ Collection of Occupant Comfort Data.” Journal of Physics. Conference Series 1343 (1): 012145.

The foundation for this project is the BUDS Lab efforts towards human sujective feedback in the built environment:

• Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models

Tutorials

Documentation found here and a shortcut to the video tutorials related to helping with the project or forking for yourself.

Download Cozie Clock Face

Latest Stable Release

Beta Development Version

License

The Cozie clockface is open-sourced under at LGPLv3 License

Copyright © 2018-2020, BUDS Lab

Run the project on your computer

Clone repository

1. first ensure that you have the latest nodejs installation https://nodejs.org/en/
2. clone the repo git clone [email protected]:buds-lab/cozie.git
3. cd cozie
4. npm install

Building and installing

1. npx fitbit-build will build the project
2. npx fitbit opens the browser, and logs into your fitbit account. From here you can connect to devices and install the app

Document structure sent by Cozie

{
"startFeedback":"2019-11-22T01:57:14.342Z",  # Timestamp when the user started the survey (i.e. pressed one of the two buttons in the clock face)
"heartRate":60,  # heart rate measured when the user completed the survey
"voteLog":40,  # counter which stores information on how many times the user completed the survey, used for debugging to check that no responses where lost
"comfort":10,  # Clock face question, 10 = "Comfy", 9 = "Not Comfy" 
"indoorOutdoor":11,  # Location, 9 = "Outdoor", 11 = "Indoor",
"change": 10, # Change location, activity or clothing, 11 = "Yes Change", 10 = "No Change"
"location": 10, # Where are you, 8 = "Portable", 9 = "Work", 10 = "Other", 11 = "Home"
"thermal":9,  # Thermal preference, 9 = "Warmer", 10 = "No Change", 11 = "Cooler"
"light":9,  # Light preference, 9 = "Brighter", 10 = "No Change", 11 = "Dimmer"
"noise":9,  # Noise preference, 9 = "Louder", 10 = "No Change", 11 = "Quiter"
"clothing":11,  # Clothing, 8="very light", 9 = "Light", 10 = "Medium", 11 = "Heavy"
"met":11,  # Metabolic rate, 8="resting", 9 = "sitting", 10 = "standing", 11 = "exercising"
"air-vel":11,  # Perceived air movement, 9 = "Not Perceived", 11 = "Perceived"
"mood":11,  # Mood, 9 = "Sad", 10 = "Neutral", 11 = "Happy"
"responseSpeed":2.577,  # Time in seconds it took to complete the survey
"endFeedback":"2019-11-22T01:57:16.919Z",  # Timestamp when the user completed the survey
"lat":48.13194,"lon":11.54944,  # Latitude and longitude provided by the GPS of the phone
"setLocation":True,
"bodyPresence":True, # passes information whether the user is wearing the watch or not
"user_id":"debug",  # User ID as per selection in settings
"experiment_id":"debug",  # Experiment ID as per selection in settings
}