In general, the motor rehabilitation process can take advantage of natural interaction based systems, including measurements from patient performance to track its evolution during time and therapy direction. Thus, the aim of this research is the analysis of the use of Kinect sensor as interaction support tool for rehabilitation systems. The Kinect sensor gives three-dimensional information about the user body, recognizing skeleton and joint positions, however does not provide the detection of the body specific movements. This way, the correct description of a rehabilitation movement (shoulder abduction, for instance) was implemented in a system prototype. A scoring mechanism was also developed in order to measure the patient performance, as well as to stimulate his improvement by displaying a positive feedback.

Emotion recognition is the task of identifying and understanding human emotions from data. In the field of computer vision, there is a growing interest due to the wide range of possible applications in smart cities, health, marketing, and surveillance, among others. To date, several datasets have been proposed to allow techniques to be trained, validated, and finally deployed to production. However, these techniques have several limitations related to the construction of these datasets. In this work, we survey the datasets currently employed in state-of-the-art emotion recognition, to list and discuss their applicability and limitations in real-world scenarios. We propose experiments on the data to extract essential insights related to the provided visual information in each dataset and discuss how they impact the training and validation of techniques. We also investigate the presence of nonverbal cues in the datasets and propose experiments regarding their representativeness, visibility, and data quality. Among other discussions, we show that EMOTIC has more diverse context representations than CAER, however, with conflicting annotations. Finally, we discuss application scenarios and how techniques to approach them could leverage these datasets, suggesting approaches based on findings from these datasets to help guide future research and deployment. With this work we expect to provide a roadmap for upcoming research and experimentation in emotion recognition under real-world conditions.

We can define Digital Musical Instruments (DMIs) as hardware-software solutions that are interactive and crafted to output sound according to users' input. DMIs are well known to unleash users' creativity but also to allow different and innovative experiences for the creation process, for example, smoothing the learning curve towards musical concepts such as rhythm and composition. On the other hand, Virtual Reality (VR) allows users to explore spatial interfaces in a natural and limitless way, which shows potential synergy towards the rise of new DMIs. In this paper, we introduce Songverse, an immersive DMI placed in a Virtual Reality scenario that allows users to create music by interacting with an environment designed to resemble the outer space. By adding systems, planets, and satellites to the virtual environment, the user can shape the produced sound through interactions that were extensively tested during the development phase. We then evaluated the instrument with musicians and non-musicians by interviewing and applying the System Usability Scale (SUS) to assess the easiness for people to create music using Songverse. As a result, users reported the use of the DMI as intuitive and easy to use, also highlighting the produced song as enjoyable.

This paper presents an open-source framework for developing guitar-based games using gesture interaction. The goal of this work was to develop a robust platform capable of providing seamless real time interaction, intuitive playability and coherent sound output. Each part of the proposed architecture is detailed and a case study is performed to exemplify its easiness of use. Some tests are also performed in order to validate the proposed platform. The results showed to be successful: all tested subjects could reach the objective of playing a simple song during a small amount of time and the most important, they were satisfied with the experience.