A NeuroFuzzy approach for fall detection
Bruno Andò, Salvatore Baglio, Vincenzo Marletta, Ruben Crispino · 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) · 2017
Falls represent a serious issue which can have catastrophic consequences and the need for ICT based solutions aimed to improve the life quality and autonomy of frail people, is hence emerging, with particular regards to falls' detection and classification. Although two main computing approaches for fall detection are threshold based algorithms and machine learning methods, it can be affirmed that up today a well-accepted solution has not been yet identified. In this paper, the experience in developing reliable fall detector of the research group working in the field of assistive technologies at the DIEEI of the University of Catania-Italy is reported, with specific regards to the investigation of a NeuroFuzzy based approach.