An Indoor Human Activity Recognition System for Smart Home Using Local Binary Pattern Features with Hidden Markov Models

Abstract

Smart home technologies are getting considerable attentions nowadays for better care of the residents, especially the elderly. One of the key technologies is the human activity recognition (HAR) system which automatically recognizes various indoor activities of a resident and reacts upon the needs of the resident, known as a proactive system. In this work, we propose a novel HAR system that utilizes depth imaging. Our HAR system utilizes local binary patterns (LBP) as local activity features from depth silhouettes and recognizes human activities via Hidden Markov Model (HMM). In our methodology, first LBP features were extracted from depth human body silhouettes from each frame of a video containing human activity. Then, principal component analysis (PCA) and linear discriminant analysis (LDA) were performed over the LBP features to obtain condensed features. Applying these features, each activity HMM was trained. Finally, HAR was performed with the trained HMMs. Our approach shows superior recognition performance over the traditional silhouette feature-based approaches. The system should be practical to be used for smart homes.