Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

O ensino em grupo de instrumentos musicais: um estudo de caso múltiplo em Portugal e no Brasil

Tese de Doutoramento em Estudos da Criança (área de especialização em Educação Musical)

O ajustamento psicossocial de jovens com consumos de substâncias psicoativas em escolas profissionais do grande Porto

Tese de Doutoramento em Estudos da Criança (Área de Especialidade Psicologia do Desenvolvimento e Educação)

Stress, cognitive appraisal, and psychological health: testing instruments for health professionals

The job of health professionals, including nurses, is considered inherently stressful (Lee & Wang, 2002; Rutledge et al., 2009), and thus it is important to improve and develop specific measures that are sensitive to the demands that health professionals face. This study analysed the psychometric properties of three instruments that focus on the professional experiences of nurses in aspects related to occupational stress, cognitive appraisal, and mental health issues. The evaluation protocol included the Stress Questionnaire for Health Professionals (SQHP; Gomes, 2014), the Cognitive Appraisal Scale (CAS; Gomes, Faria, & Gonçalves, 2013), and the General Health Questionnaire-12 (GHQ-12; Goldberg, 1972). Validity and reliability issues were considered with statistical analysis (i.e. confirmatory factor analysis, convergent validity, and composite reliability) that revealed adequate values for all of the instruments, namely, a six-factor structure for the SQHP, a five-factor structure for the CAS, and a two-factor structure for the GHQ-12. In conclusion, this study proposes three consistent instruments that may be useful for analysing nurses’ adaptation to work contexts.