Clustering-based fuzzy finite state machine for human activity recognition

Abstract

In this paper, a clustering-based fuzzy finite state machine approach for human activity modelling and recognition is proposed. It Incorporates the Fuzzy C-means (FCMs) clustering algorithm with a Fuzzy Finite State Machine (FuFSM) in order to generate the state transitions more effectively. This unsupervised approach will overcome the deficiency in identifying the knowledge-base required for FuFSM. To validate the proposed approach, experimental results are presented. The activities of two office workers are modelled/recognised using the proposed method. The approach taken for this research is based on ambient Intelligent sensory data rather than data coming from wearable sensors.