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Personalized fall detection system is shown to provide added and morebenefits compare to the current fall detection system. The personalized modelcan also be applied to anything where one class of data is hard to gather. Theresults show that adapting to the user needs, improve the overall accuracy ofthe system. Future work includes detection of the smartphone on the user sothat the user can place the system anywhere on the body and make sure itdetects. Even though the accuracy is not 100% the proof of concept ofpersonalization can be used to achieve greater accuracy. The concept ofpersonalization used in this paper can also be extended to other research inthe medical field or where data is hard to come by for a particular class. Moreresearch into the feature extraction and feature selection module should beinvestigated. For the feature selection module, more research into selectingfeatures based on one class data.

Personalized fall detection monitoring system based on learning from the user movements