Abstract
Facial expression analysis is a widespread technology applied in various research areas, including sports science. In the last few decades, facial expression analysis has become a key technology for monitoring physical exercise. In this paper, a deep neural network is proposed to recognize seven basic emotions and their corresponding probability values (scores). The score of the neutral emotion was tracked throughout the exercise and related with heart rate and power generation by a stationary bicycle. It was found that in a certain power range, a participant changes his/her expression drastically. Twelve university students participated in the sub-maximal physical exercise in stationary bicycles. A facial video, heart rate,and power generation were recorded throughout the exercise. All the experiments, including the facial expression analysis, were carried out offline. The score of the neutral emotion and its derivative was plotted against maxHR% and maxPower%. The threshold point was determined by calculating the local minima, with the threshold power for all the participants being within 80% to 90% of its maximum value. From the results, it is concluded that the facial expression was different from one individual to another, but it was more consistant with power generation. The threshold point can be a useful cue for various purposes, such as: physiological parameter prediction and automatic load control in the exercise equipment, such as treadmill and stationary bicycle. © 2020, Springer Nature Switzerland AG.

Abstract
Diabetes Mellitus (DM) is one of the most predominant diseases in the world, causing a high number of deaths. Diabetic foot is one of the main complications observed in diabetic patients, which can lead to the development of ulcers. As the risk of ulceration is directly linked to an increase of the temperature in the plantar region, several studies use thermography as a method for automatic identification of problems in diabetic foot. As the distribution of plantar temperature of diabetic patients do not follow a specific pattern, it is difficult to measure temperature changes and, therefore, there is an interest in the development of methods that allow the detection of these abnormal changes. The objective of this work is to develop a methodology that uses thermograms of the feet of diabetic and healthy individuals and analyzes the thermal changes diversity in the plantar region, classifying each foot as belonging to a DM or a healthy individual. Based on the concept of clustering, a binary classifier to predict diabetic foot is presented; both a quantitative indicator and a classification thresholder (evaluated and validated by several performance metrics) are presented. To measure the binary classifier performance, experiments were conducted on a public dataset (with 122 images of DM individuals and 45 of healthy ones), being obtained the following metrics: Sensitivity = 0.73, Fmeasure = 0.81 and AUC = 0.84.

Abstract
In the last decades due to technological advances, Robotics has change its paradigm, as well as, its course of development. Therefore, a new generation of robots has emerged, the Socially Assistive Robotics. These robots aim to improve the assistance to human users through social rather than physical interaction. Consequently, many are the developments made through the use of social robots in mental healthcare scenarios, regarding the elderly population and children or young adults with mental disorders, in order to either prevent cognitive decline, improve psycho-social outcomes or the patient capabilities and lifetime. However, few breakthroughs have been made in the mental disorders field that differ from studies with people with Autism Spectrum Disorders or that are conducted with adults. This study aims to demonstrate that humanoid NAO can improve attention in adults with mental disorders during certain task and kept through several sessions over time.

Abstract
Abstract Background: Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several attractive features as an experimental model for studying peripheral nerve regeneration. There are no studies on locomotion outcomes after peripheral nerve injury and repair in the sheep model. In the present study, we performed and compared two-dimensional (2D) and, for the first time, three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study aimed to obtain kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. Results: The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was the pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Conclusions: Our results show that the most apparent changes that occurred during the gait cycle were for the ankle and metatarsophalangeal joints, whereas the hip and knee joints were much less affected. Data and techniques described here are likely to be useful for an objective assessment of altered gait after CP injury and repair in an ovine model.

Abstract
The great potential of Unmanned Vehicles (UV) for services is supported by the evolution of drones and their flexible ability to help us performing dangerous, boring and difficult tasks. The common communication solutions e.g., radio, consider a pilot and a vehicle in the same location. Besides, the alternatives based on Ground Control Stations (GCS) are difficult to configure, which makes it difficult for a common user and a developer to use and/or implement new services for drones. This work proposes a solution with a new messaging protocol to overcome the previous problems, allowing controlling a UV in a remote location after a simple configuration. The implementation of this approach is based on a cloud platform, responsible for UV and user management, and on TCP/IP WebSockets for the user to control remotely located vehicles, anytime and anywhere. This research starts with the analysis of the prior work regarding UV communication technologies and the available GCS. Minding the identified problems, we designed an architecture that represents a cloud platform and multiple users and UVs in different networks. To prototype this architecture, we developed the user interface, the platform and the UV control module. The functional assessment considers a generic simplified and controlled scenario, focusing on the real-time control and video stream. The results confirmed the possibility to control a UV in a different location, in real-time, with an average response time of 25ms. This work provides valuable insights regarding the communication in the fields of anytime and anywhere device control and vehicle-based services.

Abstract
Accessibility is important to include people with disabilities in public life. People with disabilities experience problems in their lives due to products which aren’t designed for them. Addressing these problems is the responsibility of all humanity and the search for solutions should increase. Improving disabled persons’ lives is only possible by applying some rules to product design, such as, improving use-case scenarios. Software solutions must be considered as products in this case. Developing special software programs and upgrading existing solutions to improve the user experience for disabled persons is the obligation of all software developers. Even though the mouse is one of the most important tools, required to access webpages and many other software programs, using it can be a huge problem for people with disabilities, such as, paralysis patients. Therefore, throughout history, different types of methods have been presented as solution. Most of them are not suitable, due to various reasons, like, economic feasibility, mechanic stability and bad designs. In this work, a new method, with more in-built modern and simple technologies, is presented as an alternative to others. To outclass the good economic advantage of its competitors, a limit was set on a low budget webcam and low-tier computer specifications. The Camera tracks the user’s head movements and reacts according to it, using as measurement some metrics about the physiology of human body. Machine learning is used to extract face features and learn the user’s face to determine mouse movements. Machine learning was kept as simple as possible, in order to cope with the low system specification limitations. The main topic of the work is the design and development experience. The solution was tested with a small group of participants and their feedbacks were taken to plan for future improvements. In generally, the users’ comments were good, although some features, such as sensitivity, should be improved. The result of this work will be used to improve the software solution and hopefully will touch the life of people with disabilities. © 2020, Springer Nature Switzerland AG.