# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 1 Introduction  
Technical solutions for ambient assisted living allow continuous monitoring of vital parameters of the elderly and home automation [1, 2]. Such solutions for assisted living are particularly important in the context of an accelerated aging European society: it is estimated that by 2060, one in three Europeans will be over 65 years of age, while the ratio of active people to retired people will decrease from 4:1 currently to 2:1 [3]. Moreover, in the coming years, Europe will face more than 2 million vacancies in the health and social care system. Monitoring the environmental and physiological parameters of elderly people who suffer from chronic diseases or mental disabilities has become a topic of real interest in recent years. Both the scientific community and industry have proposed various assisted living systems for indoor environments. At the commercial level, countless assistive devices are available, such as those for administering medications, detecting falls, alarm buttons, monitoring vital parameters, as well as complex solutions for recording and tracking environmental parameters [4]. However, the solutions that integrate multiple such devices in assistance applications for autonomy at home have not yet known a commercial spread at the level of end users. Most of the integrative solutions developed so far are prototypes described in the scientific literature [5, 6, 7, 8, 9]. The main barrier to building such solutions is that most of these devices come with proprietary applications, use custom communication protocols and do not expose programming interfaces, which makes them impossible to integrate into third-party applications. The scientific literature provides many examples of complex and innovative systems for monitoring environmental and physiological parameters. For example, [PERSON] et al. [10]determines the values of physiological parameters (respiratory rate, exhaled air flow, heart rate, pulse) using images from a web camera. [PERSON] and [PERSON] [11] describe a system dedicated to environmental monitoring of air temperature, humidity, carbon monoxide and luminosity to assist people inside buildings. For this, a microsensor infrastructure is used for data acquisition, as well as Arduino, ESP8266 and XBee for data processing and transmission using Wi-Fi. The recorded data are evaluated to determine the air quality in the rooms, anticipating technical interventions. A system that analyzes the change of temperature in the environment, as well as physiological indices (temperature, breathing rate, electroencephalogram, electrocardiogram, using Japanese medical equipment from Nihonkohden Co.), is proposed in [12]. With this system, the relationships between ambient temperature, comfort sensations, sleepiness and physiological indicators were determined. Regarding integrated applications for home assistance using ICT, research efforts have focused on formulating solutions based on the combination of technologies specific to smart home digital platforms, IoT, artificial intelligence and cloud computing. The HABITAT platform [6] incorporates these technologies into everyday household objects: an armchair for monitoring sitting posture, a belt for extracting movement information, a wall panel and mobile phone for user interface. An artificial intelligence component is used for the system to react to various specific events, trigger notifications and receive feedback from the user. However, the radio frequency localization used requires the person to wear a tag, the motion monitoring belt can be uncomfortable and difficult to wear for long periods of time. In addition, the system is not easily scalable, reducing to the implementation of a single usage scenario with specific devices. A significant limitation is the specific character of these integrated systems produced to date--they address limited monitoring and application scenarios.