Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture

This work presents a general framework for people indoor activity recognition. Firstly, a Wireless Fidelity (WiFi) localization system implemented as a Fuzzy Rule-based Classifier (FRBC) is used to obtain an approximate position at the level of discrete zones (office, corridor, meeting room, etc). Secondly, a Fuzzy Finite State Machine (FFSM) is used for human body posture recognition (seated, standing upright or walking). Finally, another FFSM combines both WiFi localization and posture recognition to obtain a robust, reliable, and easily understandable activity recognition system (working in the desk room, crossing the corridor, having a meeting, etc). Each user carries with a personal digital agenda (PDA) or smart-phone equipped with a WiFi interface for localization task and accelerometers for posture recognition. Our approach does not require adding new hardware to the experimental environment. It relies on the WiFi access points (APs) widely available in most public and private buildings. We include a practical experimentation where good results were achieved.