Introducing iLog, your personal guide to understanding and enhancing your daily habits through data.
iLog
iLog is an application developed by the KnowDive group at the Department of Information Engineering and Computer Science of the University of Trento. We developed it to collect data for research purposes. With the user consent, it is able to collect data from the smartphone internal sensors and send context sensitive questions. The final goal is to study the users’ habits, allowing to react accordingly with personalized services, and generating research datasets for further studies.
User Consent and Privacy Protection
The correct management of the information collected and the participants’ privacy is of utmost importance during the use of the iLog app. For this reason, we have established the mandatory steps before any data collection with the iLog app can take place:
- Ethical Board clearance: an ethical board or similar entity, local to the institution where the survey is going to take place, has to approve the survey’s protocol. Recommendations and limitations are implemented according to their feedback.
- Institutional legal agreements signed: institutional authorities from the institutions involved in the survey agree and sign on the documents regulating how the data from the survey will be collected, stored, anonymized and used. This also includes compliance with local privacy laws, like GDPR.
- Acceptance of the Informed Consent / Terms of service before each user’ participation: iLog is configured so that just downloading and opening the app will not trigger the collection of any data from the participants’ phone. Instead, users need to input an experiment code provided by the researchers and subsequently accept the displayed Informed Consent / Terms of Use information before data collection may begin.
Your privacy is our priority. Learn more about our privacy policy here.
Availability
iLog is currently available through the Android store Google Play, and a beta version for iOS is available on the Apple AppStore
Making the Difference
- SmartUnitnOne(2017, 72 people, 2 weeks, 110GB): To gather a more comprehensive understanding of students' everyday life and how it affects their academic performance. First data collection for initial testing of the data collection platform.
- SmartUnitnTwo(2018, 300 people, 1 month, 3.0TB): To gather a more comprehensive understanding of students' everyday life and how it affects their academic performance. Main data collection to generate the needed datasets for its intended purpose.
- Qrowds(2019, 200 people, 2 weeks, 1.0TB): Understand the mobility habits of citizens from the Municipality of Trento in order to understand modal split, which will enable data-driven policies to improve transportation in Trento.
- EuroStat Hackathon(2019, 150 people, 2 weeks, ~900GB): Track the participation and organization of participants in the 2019 EuroStat Hackathon.
- WeNet Diversity Pilots(2020, 500 people, 3 weeks, ~5TB): Collect information about the student’s lifestyle in several sites of the world (Italy, Denmark, UK, Mongolia, India, Mexico, Paraguay) to map their diversity and offer insights and services to improve the quality of student life around the world.
- Mattia Zeni, Ivano Bison, Britta Gauckler, Fernando Reis, and Fausto Giunchiglia. "Improving time use measurement with personal big data collection - the experience of the European Big Data Hackathon 2019." Journal of Official Statistics, 2020.
- Zeni M, Zhang W, Bignotti E, et al. Fixing mislabeling by human annotators leveraging conflict resolution and prior knowledge[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(1): 1-23.
- Maddalena E, Ibáñez LD, Simperl E, Gomer R, Zeni M, Song D, Giunchiglia F. Hybrid Human Machine workflows for mobility management. Companion Proceedings of The 2019 World Wide Web Conference, 2019.
- Giunchiglia, F; Zeni, Mattia; Gobbi, Elisa; Bignotti, Enrico; Bison, Ivano. Mobile social media usage and academic performance. Computers in Human Behavior, vol. 82, p. 177-185, 2018.
- Giunchiglia F, Zeni M, Big E. Personal context recognition via reliable human-machine collaboration[C]//2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2018: 379-384.
- Giunchiglia F, Bignotti E, Zeni M. Personal context modelling and annotation 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2017: 117-122.
- Zeni M, Zaihrayeu I, Giunchiglia F. Multi-device activity logging Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. 2014: 299-302.
- Kim P H, Giunchiglia F. The open platform for personal lifelogging: the elifelog architecture[M]//CHI'13 Extended Abstracts on Human Factors in Computing Systems. 2013: 1677-1682.
Available on App Stores.