The 8th EAI International Conference on Mobile Computing, Applications and Services

Research Article

Ayumu: Efficient lifelogging with focused tasks

  • @INPROCEEDINGS{10.4108/eai.30-11-2016.2266867,
        author={Benjamin Stoddard and Kate O'Hanlon and Brian Lin and Ashwin Machanavajjhala and Landon Cox},
        title={Ayumu: Efficient lifelogging with focused tasks},
        proceedings={The 8th EAI International Conference on Mobile Computing, Applications and Services},
        keywords={lifelogging energy efficiency state estimation},
  • Benjamin Stoddard
    Kate O'Hanlon
    Brian Lin
    Ashwin Machanavajjhala
    Landon Cox
    Year: 2016
    Ayumu: Efficient lifelogging with focused tasks
    DOI: 10.4108/eai.30-11-2016.2266867
Benjamin Stoddard1,*, Kate O'Hanlon1, Brian Lin1, Ashwin Machanavajjhala1, Landon Cox1
  • 1: Duke University
*Contact email:


Today’s lifelogging devices capture images periodically without considering what data is important to users. Due to their small form factors and limited battery capacities, these lifeloggers are bound to miss important data either because they record at a slow rate to conserve power, or because they record at such a high rate that they must frequently recharge. In this paper, we present a new approach to lifelogging that better utilizes a device’s battery by integrating knowledge of the specific information that a user wants captured. We have developed the first instance of such a focused-task lifelogging system called Ayumu, which aims to capture the reading material that a user interacts with over the course of a day. Instead of capturing images periodically, Ayumu uses a suite of inexpensive sensors to record only when reading material is present. By recognizing when it would be most beneficial to capture images, Ayumu can achieve superior precision and comparable recall to a conventional, periodic lifelogger while using less energy.