Extended Another Memory: Understanding Everyday Lives in Ubiquitous Sensor Environments

  • Masakatsu Ohta
  • Sun Yong Kim
  • Miyuki Imada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


A lifetime recording agent that suggests unusual events to a user is proposed. The goal is to create a memory device that supports human memory by filtering, categorizing, and remembering everyday events. In ubiquitous sensor environments, the agent classifies users’ experiences represented by surrounding objects and predicts typical events that a user will experience next. Unusual events are detected by the awareness of different characteristics as the human brain does. If the prediction is incorrect, the actual event is considered to be unusual. A recurrent neural network that autonomously alters its architecture is introduced to perform event prediction. Experiments confirm: (1) a suitable hierarchical level of event categories for a current situation can be obtained by estimating the event prediction performance, that is, the recall rate and (2) rehearsal sequences dynamically generated by the network can substitute for a sequence of actual events. Thus, the agent easily responds to new environments without forgetting previous memories.


Mean Square Error Typical Event Hierarchical Level Recurrent Neural Network Training Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Masakatsu Ohta
    • 1
  • Sun Yong Kim
    • 1
  • Miyuki Imada
    • 1
  1. 1.NTT Network Innovation Laboratories, NTT CorporationTokyoJapan

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