Personalized fall detection monitoring system based on learning from the user movements
This work addresses the problem of improving fall detection accuracy for individual users, which is a significant concern for elderly care and personal safety.
This paper proposes a personalized fall detection system that adapts to individual user movements. The system demonstrates improved overall accuracy compared to non-personalized systems, particularly for scenarios where one class of data is difficult to acquire.
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.