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

TL;DR

This paper presents a personalized fall detection system that adapts to individual user movements, improving accuracy over generic models and demonstrating potential for broader applications in medical monitoring and data-scarce scenarios.

Contribution

The paper introduces a personalized fall detection approach that enhances accuracy by adapting to user-specific movement patterns, with potential extensions to other medical and data-limited fields.

Findings

Personalized models improve fall detection accuracy.

Adaptation to user needs benefits system performance.

Proof of concept supports broader application potential.

Abstract

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.