Sensor Data

Title: HealthyTogether
Contributors: Onur Yürüten and Pearl Pu
Description:

This dataset was curated to discover common physical activity routines of people and analyse the effects of social interventions on the behavior patterns. More specifically, it was curated via a longitudinal user study that involved a wearable sensor (Fitbit) and our custom mobile application called HealthyTogether. The dataset contains calorie expenditure and steps each participant to the longitudinal study.

More details on the curated data can be found in:

  • Onur Yürüten and Pearl Pu. Factoring the Habits: Comparing Methods for Discovering Behavior Patterns from Large Scale Activity Datasets. International Conference on Big Data Analytics, Data Mining and Computational Intelligence (BIGDACI). Part of Multi Conference on Computer Science and Information Systems (MCCSIS) (forthcoming) 2016
  • Onur Yürüten, Jiyong Zhang, and Pearl Pu. Decomposing Activities of Daily Living to Discover Routine Clusters. In the 28th AAAI Conference on Artificial Intelligence (AAAI-14), Quebec City, Canada, July 27-31, 2014

We distribute two version of this dataset: 
– HT-48, the original one, created as explained in the AAAI paper; 
– HT-83, where we expanded the dataset to 83 users

Links: Download HealthyTogether
Title: YQZ
Contributors: Onur Yürüten and Pearl Pu
Description:

This dataset contains the daily steps counts of 1000 participants of a social exercising campaign, who wear different sensors to measure their level of activeness. The dataset also contains the gender, height, weight, exercise group id, company id, and age (all anonymized). 

Links: Download YQZ
Title: SNACK Dataset
Contributors: Onur Yürüten and Pearl Pu
Description:

This dataset contains daily, self-reported number of snacks of a set of people. During the data collection period, these people received messages to motivate them to cut their unhealthy snacks.

Links: For more information, please contact Prof. Maurits Kaptein at Tilburg University. Relevant paper