Tailor your experiment via the Fitbit mobile app

and design your flow

Query your data programmatically

python


              import requests 

              import pandas as pd 

              import json 


              YOUR_TIMEZONE = 'Asia/Singapore' 


              payload = {'experiment-id': 'experiment', 'weeks': '30',
              'user-id': 'user01'} 


              # the api-key below is limited to 200 queries per day. Please
              contact us to get an API key 

              headers = {"Accept": "application/json", 'x-api-key':
              '7v90iALbXt1yRHdVomfpH80wmz14wTt92I0CR0na'} 


              response = requests.get(
              'https://x2nwuplc8d.execute-api.us-east-1.amazonaws.com/default/read-coziePublic-fitbitAPI',
              params=payload, headers=headers) 

              my_json = response.content.decode('utf8').replace("'", '"') 

              data = json.loads(my_json) 

              df = pd.read_json(data[1]['data']).T 

              df.index = df.index.tz_localize('UTC').tz_convert(YOUR_TIMEZONE)
              


              df.head()

Free to use

public database

full functionality

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Example Projects

Indoor Comfort Study in Singapore In this study fifteen users were given a smartwatch for one month and were prompted to give feedback on their thermal preferences. The data revealed a range of results from building anomalies, occupant behaviour, occupant personality clustering, and general feedback related to the building.

Development of personalized thermal comfort models We are currently conducting a field thermal comfort study which involves 20 participants. The aim of this project is to develop personalized thermal comfort models using wearables and IoT sensors. Participants were asked to complete an average of 6 surveys per day for a period of 6 months. The study is planned to end in Oct 2020. The results will be used to to inform the design of future sustainable buildings in the tropics.