The Memory Glasses: Subliminal vs. Overt Memory Support with Imperfect Information
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Software or wetware?: discovering when and why people use digital prosthetic memory
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Nonintrusive appliance load monitoring: Review and outlook
IEEE Transactions on Consumer Electronics
Understanding domestic energy consumption through interactive visualisation: a field study
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.