LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by $n=71$ participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

LifeSnaps-EDA

A repository for sharing the Exploratory Data Analysis (EDA) of the LifeSnaps dataset collected during the RAIS Experiment. This repo includes notebooks regarding the preprocessing and visualizations of the LifeSnaps dataset. The preprocessing notebooks load the LifeSnaps dataset from the MongoDB database creating the final unified dataframe in daily and hourly granularity to be used for any further analysis. The visualizations notebooks describe the LifeSnaps dataset and inlcude all the figures presented in the paper submitted for peer-review. bar_charts_semas notebook depicts the daily and weekly answers of the Context and Mood SEMA questionnaires, heatmap_all_fitbit_types includes heatmaps showing the availability of every Fitbit data type on a daily basis, heatmap_fitbit_data_availability includes also heatmaps showing the availability of three main Fitbit types (steps, distance and estimated oxygen variation) on an hourly basis throughout the days of the week, heatmaps_semas gives a heatmap depicting when did the participants choose to answer the Context and Mood SEMA questionnaires and user_engagement_fitbit_sema_surveys combines all three modalites used to collect the data (Fitbit, SEMA and Surveys) and describes the user engagement throughout the study days.

Code Availability

Data Anonymization: https://github.com/syfantid/RAIS-Anonymization

Exploratory Data Analysis: https://github.com/kcristinaa/LifeSnaps-EDA

Publications

Sofia Yfantidou, Christina Karagianni, Stefanos Efstathiou, Athena Vakali*,* Joao Palotti, Dimitrios Panteleimon Giakatos, Thomas Marchioro, Andrei Kazlouski, Elena Ferrari, and Sarunas Girdzijauskas, 2022, LifeSnaps: a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild (Submitted for peer-review).

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813162. The content of this paper reflects only the authors' view and the Agency and the Commission are not responsible for any use that may be made of the information it contains. First and foremost, the authors would like to thank the participants of the LifeSnaps study who agreed to share their data for scientific advancement. The authors would like to further thank the web developers, T. Valk and S. Karamanidis, for their contribution to the project, all past and present RAIS fellows for their help with participants' recruitment, G. Pallis and M. Christodoulaki for their support with the ethics committee application, and B. Carminati for her feedback on data anonymization and privacy considerations.