Abstract

Abstract
Na viragem do milénio, decorrentes sobretudo do Processo de Bolonha e da (r)evolução tecnológica, colocaram-se ao Ensino Superior e aos docentes vários desafios, nomeadamente os relacionados com questões pedagógicas. Tendo essa situação por referência, o artigo apresenta um estudo que analisou a produção académica em Portugal entre 2012-2021, pesquisada com recurso a diversas bases de dados e interpretada por técnicas de análise de frequência e de conteúdo. A análise evidenciou que: o número de artigos publicados em revistas indexadas ou em atas de conferências não foi tão expressivo como a urgência da reflexão que a temática merecia; o número de teses de doutoramento e dissertações de mestrado com foco nas questões pedagógicas no Ensino Superior foi relativamente reduzido; a metodologia desses trabalhos, embora distribuída entre procedimentos de orientação qualitativa, mista e quantitativa, teve menor expressão neste último tipo metodológico; muitos dos autores dos trabalhos académicos são do campo educacional, embora existam também autores de outras áreas científicas; a maior parte dos trabalhos académicos teve como foco a formação pedagógica dos docentes do Ensino Superior, a que se seguiram estudos relativos a práticas pedagógicas e a desafios colocados ao Ensino Superior. A par dessas conclusões, o estudo permitiu identificar limitações que justificam o seu aprofundamento, mas também recomendações para futuros trabalhos de investigação sobre essa temática.

Abstract

Abstract
Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

Abstract

Abstract
Objective: Stepping exergames designed to stimulate physical and cognitive skills can provide important information concerning individuals’ performance. In this study, we investigated the potential of stepping and gameplay metrics to assess the motor-cognitive status of older adults. Methods: Stepping and gameplay metrics were recorded in a longitudinal study involving 13 older adults with mobility limitations. Game parameters included games’ scores and reaction times. Stepping parameters included length, height, speed, and duration, measured by inertial sensors placed on the shoes while interacting with the exergames. Parameters measured on the first gameplay were correlated against standard cognitive and mobility assessments, including the Montreal Cognitive Assessment (MoCA), gait speed, and the Short Physical Performance Battery. Based on MoCA scores, patients were then stratified into two groups: cognitively impaired and healthy controls. The differences between the two groups were visually inspected, considering their within-game progression over the training period. Results: Stepping and gameplay metrics had moderate-to-strong correlations with cognitive and mobility performance indicators: faster, longer, and higher steps were associated with better mobility scores; better cognitive games’ scores and reaction times, and longer and faster steps were associated with better cognitive performance. The preliminary visual analysis revealed that the group with cognitive impairment required more time to advance to the next difficulty level, also presenting slower reaction times and stepping speeds when compared to the healthy control group. Conclusion: Stepping exergames may be useful for assessing the cognitive and motor status of older adults, potentially allowing assessments to be more frequent, affordable, and enjoyable. Further research is required to confirm results in the long term using a larger and more diverse sample. © The Author(s) 2023.