A review on applications of activity recognition systems with regard to performance and evaluation

Abstract

Activity recognition systems are a large field of research and development, currently with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while increasing safety. This article concentrates on the applications of activity recognition systems and surveys their state of the art. We categorize such applications into active and assisted living systems for smart homes, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and tele-immersion applications. Within these categories, the applications are classified according to the methodology used for recognizing human behavior, namely, based on visual, non-visual, and multimodal sensor technology. We provide an overview of these applications and discuss the advantages and limitations of each approach. Additionally, we illustrate public data sets that are designed for the evaluation of such recognition systems. The article concludes with a comparison of the existing methodologies which, when applied to real-world scenarios, allow to formulate research questions for future approaches.