## 1 Introduction
Depending on the context in which the system must operate and the needs of the subject to be monitored, one or more sensors can be used and deployed for collecting behavioral and personal data. Several types of sensors can be exploited: audio (i.e., microphones), visual (i.e., 2D and/or 3D cameras), environmental (i.e., pressure, infrared, radar,...), and physiological (i.e., blood pressure, body temperature,...). The data acquired by the sensors is stored in a database system for logging and analysis. The data can be continuously processed to detect anomalous activities or danger situations that require intervention. The data can also be used to perform long-term monitoring and analysis of the activities by the family members, caregivers, or experts. Different monitoring systems have been proposed in the literature and have been surveyed in [6; 7; 8]. These systems mostly differ by the sensors used for collecting the raw data, i.e., single vs. multiple sensors and mono or multi-modal sensing devices, and in the final aim, i.e., registering specific activities, daily logging or detecting dangerous situations. All these systems have basic architectures similar to the one shown in Figure 3.  
Systems that exploit wearable sensors can be perceived as being intrusive by the users, while systems based on ambient sensors requires the installment of specific hardware in the rooms of the house that can be also problematic on existing buildings. In this work, we tackle the problem of monitoring elderly people at home by exploiting computer vision technologies and visual sensors such as RGB cameras. More specifically, we designed an assisted living monitoring system to record and analyze daily activities of elderly people at home. The system is designed to be as unobtrusive as possible and thus it does not require the person to wear sensors. Instead it relies on a camera to collect video streams that are processed to recognize and store the target's behaviors. For this work we envision a single-camera scenario, where the acquisition device is located in a strategic position, possibly in the living room above a TV screen. To this extent, we provide experiment-based recommendations about the suggested location, driven by the estimated maximum distance between the camera and the subject. The natural extension of this setup involves installing multiple cameras in the house, possibly including 360\({}^{\circ}\) cameras, and leveraging on person  
Figure 3: General architecture of an Ambient Assisted Living system. re-identification techniques to provide a consistent analysis, although we reserve this development for future works. To perform the monitoring, the system integrates advanced action recognition algorithms that are robust for the indoor scenario. The output of the monitoring is then used to provide alert messages in case of anomalous events that can be selected by the user. We designed our system to be accessible and reliable. We also propose an application to support raw data collection, activity monitoring, visual log generation, and support for anomaly inference and alerting, through a user-friendly interface. The rest of the paper is organized as follow: Section 2 presents an overview of the existing action recognition methods and systems, ending with an overview of commonly used action recognition datasets. In Section 3 we describe the creation of our reference dataset of actions that will be used for the design and evaluation of action recognition algorithms. In Section 4, we introduce our proposed monitoring system based on action recognition, and how we approached the subject localization and the recognition of the action. Experimental results are described and analyzed in Section 5. In Section 6 we present a client-server application based on our monitoring system. Finally, Section 7 concludes the paper.