  Personalized fall detection system is shown to provide added and more
benefits compare to the current fall detection system. The personalized model
can also be applied to anything where one class of data is hard to gather. The
results show that adapting to the user needs, improve the overall accuracy of
the system. Future work includes detection of the smartphone on the user so
that the user can place the system anywhere on the body and make sure it
detects. Even though the accuracy is not 100% the proof of concept of
personalization can be used to achieve greater accuracy. The concept of
personalization used in this paper can also be extended to other research in
the medical field or where data is hard to come by for a particular class. More
research into the feature extraction and feature selection module should be
investigated. For the feature selection module, more research into selecting
features based on one class data.

Machine Learning