Unsupervised Learning Fuzzy Finite State Machine for Human Activities Recognition

Abstract

Human Activities Recognition (HAR) based on low-level sensory data has become an active research topic and attracting attention in many application domains. Many approaches are employed to process and analyse the collected sensory data for modelling and representing Activity of Daily Working (ADW) and/or Activity of Daily Living (ADL). In this paper, a novel method based on Fuzzy Finite State Machine (FuFSM) is presented to model the daily activities. The proposed method is using FuFSM integrated with Fuzzy C-Means (FCMs) clustering algorithm to overcome the challenges of defining simultaneous activities. Therefore, different states of activities could be represented with a degree of fuzziness. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a set of data that has been collected from an office environment.