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Toward Construction of Wearable Sensing Environments

  • Kazuya Murao
  • Tsutomu Terada
  • Shojiro Nishio
Part of the Studies in Computational Intelligence book series (SCI, volume 278)

Abstract

The downsizing of computers has led to wearable computing devices that have attracted a great deal of attention. A wearable computer in wearable computing environments runs various applications with various sensors (wearable sensors). The beginning of this chapter describes the present situation with wearable sensing by giving examples of several applications using state-of-the-art technologies, and outlines underlying problems with wearable sensing. Even though low-power consumption is an important issue since the sensors are connected wirelessly and their batteries should be small due to restrictions with size and weight in a wearable sensing environments, conventionalwearable systems did not flexibly control power supply and consumed excess power resources for unused sensors. Additionally, sensors frequently become unstable by several reasons such as breakdown of sensors. As a solution to these, we introduce a wearable sensor management device CLAD that has various functions for power management and sensed data management. The latter half of this chapter describes its application with taking power-reduction in context-aware systems as an example. Even though various systems using accelerometers have been proposed to recognize very minute motions and states in the area of context awareness, the energy consumption is not taken into consideration. In actual life, the granularity of contexts that the users require differs according to their situations. Therefore, the proposed system changes the granularity of cognitive contexts on their situations and supplies power on the basis of the optimal

Keywords

Support Vector Machine Power Consumption Active Sensor Context Group Context Transition 
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 2010

Authors and Affiliations

  • Kazuya Murao
    • 1
  • Tsutomu Terada
    • 2
  • Shojiro Nishio
    • 1
  1. 1.Department of Multimedia Engineering, Graduate School of Information Science and TechnologyOsaka UniversityJapan
  2. 2.Department of Electrical and Electronic Engineering, Graduate School of EngineeringKobe UniversityJapan

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