• DocumentCode
    2663179
  • Title

    Power and size optimized multi-sensor context recognition platform

  • Author

    Bharatula, Nagendra B. ; Stäger, Mathias ; Lukowicz, Paul ; Tröster, Gerhard

  • Author_Institution
    Wearable Comput. Lab., Eidgenossische Tech. Hochschule, Zurich, Switzerland
  • fYear
    2005
  • fDate
    18-21 Oct. 2005
  • Firstpage
    194
  • Lastpage
    195
  • Abstract
    This paper presents a miniaturized low-power platform for real-time activity recognition. The wearable sensor system comprises of accelerometers, a microphone, a light sensor and signal processing units. The recognition is performed with low-power features and a decision tree classifier. Power measurements show that our 4.15×2.75 cm2, 9 gram platform consumes less than 3 mW and can perform continuous classification and result transmission for 129 hours on a small lithium-polymer battery.
  • Keywords
    mobile computing; optimisation; pattern recognition; power consumption; sensor fusion; decision tree classifier; lithium-polymer battery; miniaturized low-power platform; multisensor context recognition; power measurement; real-time activity recognition; wearable sensor system; Accelerometers; Batteries; Classification tree analysis; Decision trees; Microphones; Performance evaluation; Power measurement; Sensor systems; Signal processing; Wearable sensors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wearable Computers, 2005. Proceedings. Ninth IEEE International Symposium on
  • Print_ISBN
    0-7695-2419-2
  • Type

    conf

  • DOI
    10.1109/ISWC.2005.42
  • Filename
    1550807
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2663179