Abstract
Physical activity is movement of the body or part of the body to make muscles more active and to lose the energy from the body. Regular physical activity in the daily routine is very important to maintain good physical and mental health. It can be performed at home, a rehabilitation center, gym, etc., with a regular monitoring system. How long and which physical activity is essential for specific people is very important to know because it depends on age, sex, time, people that have specific diseases, etc. Therefore, it is essential to monitor physical activity either at a physical activity center or even at home. Physiological parameter monitoring using contact sensor technology has been practiced for a long time, however, it has a lot of limitations. In the last decades, a lot of inexpensive and accurate non-contact sensors became available on the market that can be used for vital sign monitoring. In this study, the existing research studies related to the non-contact and video-based technologies for various physiological parameters during exercise are reviewed. It covers mainly Heart Rate, Respiratory Rate, Heart Rate Variability, Blood Pressure, etc., using various technologies including PPG, Video analysis using deep learning, etc. This article covers all the technologies using non-contact methods to detect any of the physiological parameters and discusses how technology has been extended over the years. The paper presents some introductory parts of the corresponding topic and state of art review in that area.

Abstract
The Industry 4.0 (I4.0) paradigm comes as a direct action by the German government to improve the industrial production process, by enhancing the ability to take action during the process and produce customized products, while maintaining the mass production industrial context. The I4.0 solutions rely on cybernetics systems that can enhance the users' decision-making. Some technologies are particularly suited for this purpose, including data science combined with context sensitive applications and virtual assistants (VA). These types of user application can provide information and features according to the user's context, thus acting proactively and foreseeing the user actions. In this work, we partnered with Continental Advanced Antenna - a manufacturer of radiofrequency devices for the auto industry, to further develop the concept of a VA to assist the production management. A prototype was built to interact and keep the production management team up to date regarding the ongoing execution of the production plan.

Abstract
The use of mobile conversations is increasing all around the world. A conversational agent (CA) is mostly useful due to the fast response times and their simple nature. Recently, we have seen the development and increasing use of dialog systems on the Web. A conversational agent (CA) is a system capable of conversing with a user in natural language, in a way that it simulates a human dialog. Examples of CA can be found in several areas, including healthcare, entertainment, business, and education. In this paper a state of the art review of these dialog systems is presented, comprising different categories, different approaches and trends. The purpose of this work is to identify and compare the main existing approaches for building CA, categorizing them and highlighting the main strengths and weaknesses. Furthermore, it seeks to contextualize their use in an educational context and to discover the issues related to this task that may help in the choice of future investigations in the area of conversational natural language processing in educational context.

Abstract
A typical example of a Brain-Computer Interface (BCI) is a system that allows a person to move a ball displayed on a computer screen to the left or to the right, simply by imagining the movement of the left or right hand, respectively. Since the term Brain-Computer Interface was coined in 1973, the interest and efforts in this field have grown tremendously and there are now thought to be several hundred laboratories worldwide developing research in this topic. This paper aims at summarizing its resulting knowledge in a way that allows for a quick and clear consultation, highlighting the research lines, technologies and the most relevant cases of applications, so that policy makers, professionals and consumers can make effective use of the findings. With this in mind, a Brain-Computer Interface toolkit is proposed with a focus on different target audiences (e.g., children, seniors, people with intellectual disabilities) that can take advantage of this resource and promote an independent life routine. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper reviewed machine learning algorit hms, particularly deep learning architectures applied to end-of-line testing systems in industrial environment. In industry, data is also produced when any product is being manufactured. All this information registered when manufacturing a specific product can be manipulated and interpreted using Machine Learning algorithms. Therefore, it is possible to draw conclusions from data and infer valuable results that can positively impact the future of the production line. The reviewed papers showed that machine learning algorithms play a crucial role in detecting, isolating, and preventing anomalies, helping operators make decisions, and allowing industries to save resources. © International Journal of Emerging Technology and Advanced Engineering.All right reserved.

Abstract
Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.