%Aigaion2 BibTeX export van HES SO Valais Publications
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@INPROCEEDINGS{,
        author = {Genoud, Dominique and Cuendet, Vincent and Torrent, Julien},
         month = mar,
         title = {SOFT FALL DETECTION USING MACHINE LEARNING in WEARABLE DEVICES},
     booktitle = {AINA},
          year = {2016},
      location = {Crans Montana, Switzerland},
  organization = {IEEE},
      abstract = {Wearable watches provide very useful linear acceleration information that can be use to detect falls. However
falls not from a standing position are difficult to spot among
other normal activities. This paper describes methods, based
on pattern recognition using machine learning, to improve the
detection of “soft falls”. The values of the linear accelerometers
are combined in a robust vector that will be presented as input
to the algorithms. The performance of these different machine
learning algorithms is discussed and then, based on the best
scoring method, the size of the time window fed to the system
is studied. The best experiments lead to results showing more
than 0.9 AUC on a real dataset. In a second part, a prototype
implementation on an Android platform using the best results
obtained during the experiments is described.}
}