Human Activity Recognition Framework in Monitored Environments
Olmo León, Manuel Pegalájar Cuéllar, Miguel Delgado‐Rodríguez, Yann‐Aël Le Borgne, Gianluca Bontempi · 2014
This work addresses the problem of the recognition of human activities in \textit{Ambient Assisted Living (AAL)} scenarios. The ultimate goal of a good AAL system is to learn and recognize behaviours or routines of the person or people living at home, in order to help them if something unusual happens. In this paper, we explore the advances in unobstrusive depth camera-based technologies as a single sensor to detect human activities involving motion. We develop a model for learning and recognizing seven basic human actions (walk, sit down, stand up, bend down, bend up, twist left, and twist right). Our approach is composed of 5 steps ((a) body representation, (b) time series summarization, (c) posture clustering-quantization, (d) action learning with Hidden Markov Models, and (e) action recognition). The results obtained suggest that this type of sensors are accurate enough and useful to achieve high score for detection using classic stochastic models as behaviour representation and recognition.