Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity

Steve Williams, J. Mark Ware, Berndt Müller

Research output: Contribution to journalConference articlepeer-review

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Abstract

A large proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates depends on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentifiable people? Our research shows that deep learning is possible using relatively low capacity computing. The research demonstrates promising results in recognition of human geospatial activity, in prediction of movement, and assessment of contextual risk when applied to spatio-temporal positioning of human subjects. A private surveillance system is thought particularly suitable in the care of those who may, to some, be considered vulnerable.

Original languageEnglish
Pages (from-to)212-223
Number of pages12
JournalCEUR Workshop Proceedings
Volume2142
Publication statusPublished - 1 Jun 2018
Event1st Joint Workshop on AI in Health, AIH 2018 - Stockholm, Sweden
Duration: 13 Jul 201814 Jul 2018

Keywords

  • Assisted-living
  • Deep learning
  • Dementia
  • Ethics
  • GPS
  • LSTM
  • MHeath
  • Mobile computing
  • Privacy
  • RNN
  • Safer walking
  • Wearable health

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