@article{brezmesActivityRecognitionAccelerometer2009,
  title = {Activity Recognition from Accelerometer Data on a Mobile Phone},
  volume = {5518 LNCS},
  issn = {03029743},
  url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.1333&rep=rep1&type=pdf%5Cnhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.92.1333},
  doi = {10.1007/978-3-642-02481-8_120},
  abstract = {Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic human movements using a conventional mobile phone equipped with an accelerometer. The aim of this study was to check the present capacity of conventional mobile phones to execute in real-time all the necessary pattern recognition algorithms to classify the corresponding human movements. No server processing data is involved in this approach, so the human monitoring is completely decentralized and only an additional software will be required to remotely report the human monitoring. The feasibility of this approach opens a new range of opportunities to develop new applications at a reasonable low-cost},
  issue = {PART 2},
  journaltitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  date = {2009},
  pages = {796--799},
  keywords = {Accelerometer,Pattern recognition,Human movement's detection},
  author = {Brezmes, Tomas and Gorricho, Juan Luis and Cotrina, Josep},
  file = {/home/dimitri/Nextcloud/Zotero/storage/MWYEJD5U/Ravi et al. - 2005 - Activity recognition from accelerometer data.pdf}
}

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