Abstract
This article is devoted to the problem of training cyclists from a system approach. Technologies were used to monitor and evaluate in an integrated way the physical form, load parameters and level of functional capabilities of the athlete's body. For correlation between the physiological indices and performance, data from cardiac activity (ECG), muscle activity (EMG and temperature), respiratory processes (oximetry), as well as data from the environment where this athlete is inserted (ambient temperature, pressure, humidity).

Abstract
The continuous use of the muscles in any kind of physical exercises results in muscular fatigue, which can be defined as the incapability of the muscle to perform with the same effectiveness over the course of time. The analysis of physical exercise intensity has great importance in various fields, including sports and physiotherapy. In this paper, the rate of blinking eyes and the change in shape of mouth throughout the physical exercise are analyzed using computer vision techniques, and compared with the perceived exertion. The experiments were done using the facial video of three athletes, grabbed during a stationary cycle of physical exercise, until maximal muscle activity was achieved. The perceived exertion was reported at the end of each minute. The blinking of the eyes and opening of the mouth were detected by counting the number of bright pixels in the region of interest of an eye and of the mouth. These regions were detected using the Viola and Jones algorithm. We have proved the existence of a correlation between the opening and closing of the mouth and the eye-blinking rates with the physical exercise intensity (i.e., the higher the exercise intensity, the higher the rate of eye-blinking and mouth opening and closing). We obtained 95% accuracy in blinking eye detection.

Abstract
Aging is a natural process during which a person's physical and mental condition will degrade, imposing limitations on their lifestyle. The consequences of aging can be aggravated when the elderly permanently move to an elderly care center, causing a decline in social relations and contact with family and friends, with negative effects on the person's well-being. Therefore, it is important to act to maintain these relationships in the later stages of life. In this work we propose a roadmap to introduce and evaluate the usage of telepresence robots in elderly care centers, so that family and friends can interact with the elderly more often and with better quality. The proposal defines a set of steps to introduce the robots into the care center environment, as well as an evaluation methodology to assess the success of implementing the roadmap.

Abstract
Many cloud vision APIs are available on the internet to recognize emotion from facial images and video analysis. The capacity to recognize emotions under various poses is a fundamental requirement in the area of emotion recognition. In this paper, the performance of two famous emotion recognition APIs is evaluated under the facial images of various poses. The experiments were done with the public dataset containing 980 images of each type of five poses [full left, half-left, straight, half-right, and full-right] with the seven emotions (Anger, Afraid, Disgust, Happiness, Neutral, Sadness, Surprise). It has been discovered that overall recognition accuracy is best in Microsoft Azure for straight images, whereas the face detection capability is better in Google. The Microsoft did not detect almost any of the images with full left and full right profile, but Google detected almost all of them. The Microsoft API presents an average true positive value up to 60%, whereas Google presents the maximum true positive value 45.25%.

Abstract
Kinematic analysis of human movement is very important in several areas, such as in sports (e.g., for athletic performance analysis), health (e.g., rehabilitation of people with motor disabilities) and others. The study of the kinematics of the human body involves several methods that resort to the analysis of several parameters that come from the movement. Important parameters to take into account are the acceleration, velocity and position (linear or angular) of the various articulations of the human body, which can be measured by sensors or through the analysis of repeated images obtained by camera. In this paper will be presented a suit that acquire the different position of human joints that will be useful in rehabilitation, FatoXtract. It is through the analysis of human movement that we can analyze whether the movement in rehabilitation is adequate or not.

Abstract
AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness); number of errors made, as well as number and type of difficulties found (efficiency); and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface.