Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly

  • Roberto Luis Shinmoto TorresEmail author
  • Damith C. Ranasinghe
  • Qinfeng Shi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 131)


The development of human activity monitoring has allowed the creation of multiple applications, among them is the recognition of high falls risk activities of older people for the mitigation of falls occurrences. In this study, we apply a graphical model based classification technique (conditional random field) to evaluate various sliding window based techniques for the real time prediction of activities in older subjects wearing a passive (batteryless) sensor enabled RFID tag. The system achieved maximum overall real time activity prediction accuracy of \(95\,\%\) using a time weighted windowing technique to aggregate contextual information to input sensor data.


Conditional random fields RFID Feature extraction 


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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Roberto Luis Shinmoto Torres
    • 1
    Email author
  • Damith C. Ranasinghe
    • 1
  • Qinfeng Shi
    • 1
  1. 1.Auto-ID Lab, School of Computer ScienceThe University of Adelaide South AustraliaAdelaideAustralia

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