An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

Abstract

This chapter presents an assessment of falls and everyday situations in people by sensors dataset collected in fall simulation. This evaluation was performed through the use of intelligent techniques and models based on feature selection techniques and fuzzy neural networks. Therefore, this work can be seen as an auxiliary approach of presenting a vision of knowledge extraction for the construction of actions, prevention, and training to functional that will work in areas correlated to health impacts of people who may have difficulties or injuries due to the impact suffered in a fall. The results obtained were compared with state of the art for the theme and the version of the hybrid model that acts on the most relevant dataset dimensions identifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people.