A context-aware experience sampling tool
CHI '03 Extended Abstracts on Human Factors in Computing Systems
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones
Proceedings of the 5th international conference on Mobile systems, applications and services
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
The social fMRI: measuring, understanding, and designing social mechanisms in the real world
Proceedings of the 13th international conference on Ubiquitous computing
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Contextual dissonance: design bias in sensor-based experience sampling methods
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Contextual dissonance: design bias in sensor-based experience sampling methods
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
How do you feel?: your computer knows
Communications of the ACM
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The ubiquity of sensor-rich and computationally powerful smartphones makes them an ideal platform for conducting social and behavioural research. However, building sensor data collection tools remains arduous and challenging: it requires an understanding of the varying sensor programming interfaces as well as the research issues related to building sensor-sampling systems. To alleviate this problem and facilitate the development of social sensing and data collection applications, we are developing a set of open-source smartphone libraries to collect, store and transfer, and query sensor data. Furthermore, we have also developed a library that can trigger notifications based on time or sensor events to assist experience sampling methods. This paper presents these libraries' architecture, initial feedback from developers using it, and a sensing application that we built using them to study daily affect.