# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 1 Introduction
A very relevant research and development topic is addressing the implementation and interoperability issues encountered in the adoption of life assistance technology. In this sense, possible solutions were formulated for managing the integration of devices and aggregating information from them [7; 8]. However, the proposed solutions require significant integration efforts or the involvement of device manufacturers in a common standard alignment process. A relevant effort in this sense is represented by ACTIVAGE [9], a pilot project for intelligent living environments with the main goal of building the first European eco-system. It will have nine implementation locations in seven European countries, reusing and scaling public and private IoT platforms. New interfaces necessary for interoperability are being integrated between these platforms that will implement IoT solutions for active and healthy aging on a large scale. The project will deliver AIOTES (AC-TIVAGE IoT Ecosystem Suite), a set of mechanisms, tools and methodologies for interoperability at different levels between IoT platforms and a free working method for providing their semantic interoperability. A monitoring system designed for a health care environment has the following components: data exchange between devices, data storage, data processing to determine environmental conditions and physiological aspects, data security and confidentiality, as well as data access to them [1; 2]. According to [PERSON] [13], there are two types of information: unprocessed data from the sensors and context data used to determine behavior patterns or human activity. The context is important because it helps to evaluate environmental conditions and the health of the person monitored. The system must provide a way to represent the environment. Some solutions only have a 2D map [14; 15], while others use 3D models [16; 17] from which essential information can be extracted (door and window locations, pieces of furniture, gas sources, etc.) and generate positional alerts. In addition to processing sensor data, a monitoring system must integrate communication technologies in order to provide real-time health services that are aligned with the context and the real needs [18]. In an IoT system, nodes containing sensors are used to monitor the user, to collect data which is subsequently sent to a network of nodes. The pressure put on the health insurance systems by care services for the elderly or people with special needs is increasing. Therefore, an automated system that can assist a person in his environment would be less expensive. Such a system is based on different types of sensors, independent or within a network. [PERSON], [PERSON] and [PERSON] in [19] review monitoring systems that use different sensors, such as cardiogram sensors for heart rate, sensors for breath measurements, for blood pressure, and for body temperature,. This paper presents a monitoring system for daily activities based on Visual Scene Understanding. This paper is organized as follows: in Section 2, different approaches for human activity recognition are presented. In Section 3, we will find a short introduction to the SmartCare system, after which we discuss the integration of the HAR module into the project. In this section are also presented the hardware architecture, the monitoring scenarios for various medical conditions and the results on object detection and HOI detection with the selected models. In Section 4, the datasets used for object detection as well as for HOI detection are presented. In Section 5, we shortly present de HAR module architecture, while in Section 6 the design and implementation of the HAR module is presented; a comparison between the tested object detectors in the context of human-object interaction and the use of the HAR results within the SmartCare are also discussed here. Finally, we draw some conclusions in Section 7.