Large and bright screens on today's mobile phones account for significant energy demand on phones' batteries. In this paper we propose an algorithm that, given the energy profile of the screen, finds the optimal schedule to minimize screen energy dissipation when the phone is idle. We profile the screen energy consumption of two popular smartphones, Nokia N95 and E71, through carefully designed micro-benchmarks. Our energy measurement results suggest that the default screen schedules on these phones are far from optimal - on average our algorithm performs 50% better than default. We also find that on the E71 not using the dim state of the screen and directly turning it off is more energy-efficient. We improve the performance of our screen scheduling algorithm by considering the history of each user's interaction with his/her phone. We study the interaction patterns of six volunteers with their smartphones. The results suggest that the distribution of the length of idle times for each user does not change over time. Therefore, the screen scheduler can learn this distribution during a learning phase and use it to improve screen management. We show that the screen energy consumption can be further reduced by up to 60% using this technique.

Advances in technology and infrastructure have positioned mobile phones as a convenient platform for real-time assessment of an individuals health and behavior, while offering unprecedented accessibility and affordability to both the producers and the consumers of the data. In this paper we address several of the key challenges that arise in leveraging smartphones for health: designing the complex set of building blocks required for an end-to-end system, motivating participants to sustain engagement in long-lived data collection, and interpreting both the data and the quality of the data collected.

We present AndWellness, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. In order to address the system design and participation motivation challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. AndWellness additionally includes rich system and user analytics to instrument the act of participation itself and ultimately to contextualize and better understand the factors affecting the quality of collected data over time. We evaluate the usability and feasibility of AndWellness using data from 3 studies with a variety of populations including young moms and recent breast cancer survivors. More than 85% of the diverse set of participants who responded to exit surveys claim they would use AndWellness for further personal behavior discovery.

overview poster so no abstract.