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.

The reduction in physical and social activity in elderly people degrades the aging process, causing or increasing suffering to the individual and to his family and friends. Most times, the individual well-being is related to the strength of the social bonds with the family and friends group, so it is important to keep this bonds in the various stages of life, especially in the later life, when the individual has health limitations and is living in a community centre. This paper proposes an exploratory approach, regarding the use of information technologies systems, as autonomous systems, to assist in supporting and encouraging elderly people to preserve and develop their social activities and the relationships with their family and friends social group. During the course of the aging process, some aging people have their social activity degraded due to health limitations and institutionalization in elderly care centers, in which they are admitted as residents. We propose the design of a software system, capable of running in autonomous devices, such as, robots and other consumer appliances, enabling them to recognize and interact with the users, according to their state of mind and the specific current moment context. The interactions should be simple and focused on keeping the users informed about the current life events of their family and friends, and create the opportunities for the users to interact with the participants of those events by creating interaction proposals. The systems would monitor the user's family and friends group, regarding their information and actions on the social media. The system's interactions with the user would be based on that knowledge and on the knowledge of the user's current context. An adaptive user interface would present and manage the user interactions, acting as a mediator between the user and his family and friends group.

Facial expressions are a spontaneous way of perceiving emotions, which can provide information related to the cognitive state of a person. Facial expression recognition of the elderly is an important aid to better care them, according to their state of mind, although it can be a difficult task because their expressions might not be as easily perceived as those from younger persons. We proposed a model to classify the facial expressions of the elderly, presenting the differences between facial expression recognition in the elder and in other age group, as well as methods to surpass these difficulties. Viola Jones with Haar Features was used to extract the faces and Gabor Filter to extract the facial characteristics. These characteristics are classified using a Multiclass Support Vector Machine. We got an accuracy of 90.32%, 84.61%and 66.6%, when detecting the neutral state, happiness and sadness respectively in the elderly. In the other age group, we got an accuracy of 95.24%, 88.57%, and 80%, while detecting the neutral, happiness, and sadness states and concluded that aging influences negatively the facial expressions recognition tasks.