A behavior model for persuasive design
Proceedings of the 4th International Conference on Persuasive Technology
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Home, habits, and energy: examining domestic interactions and energy consumption
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing eco-feedback systems for everyday life
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Investigating intelligibility for uncertain context-aware applications
Proceedings of the 13th international conference on Ubiquitous computing
Learning from a learning thermostat: lessons for intelligent systems for the home
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
| Hi-index | 0.00 |
In order to better understand the opportunities and challenges of an intelligent system in the home, we studied the lived experience of a thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. To date, we have conducted six interviews and one diary study. Our findings show that improved interfaces through web and mobile applications changed the interactions between users and their home system. Intelligibility and accuracy of the machine learning and sensing technology influenced the way participants perceive and adapt to the system. The convenient control over the system combined with limitations of the technology may have prevented the desired energy savings. These findings assert that thoughtful, continuous involvement from users is critical to the desired system performance and the success of interventions to promote sustainable choices. We suggest that an intelligent system in the home requires improved intelligibility and a better way in which users can provide deliberate input to the system.