Patients with Parkinson's disease (PD) often exhibit speech and voice impairments early in the disease course, making these characteristics potential biomarkers for diagnosis. Additionally, PD speech analysis offers a promising avenue for monitoring disease progression in response to therapeutic interventions. In this study, we propose a novel method for distinguishing PD patients from healthy controls (HCs) through the analysis of speech task recordings. Our method integrates recurrence plots (RPs) and their corresponding quantitative descriptors with established speech features, namely Mel-spectrograms and Mel-frequency Cepstral Coefficients (MFCCs). To enrich the representation of speech signals, RPs and Mel-spectrograms are further processed to extract features using a Convolutional Neural Network (CNN). The resulting feature sets are then classified with a Support Vector Machine (SVM). Experimental evaluations on the PC-GITA speech database, as well as an analogous set of PD tasks in Greek, demonstrate the effectiveness of the proposed approach, achieving classification accuracies above 90% on the examined tasks.

During the last decades, the problem of speech emotion recognition (SER) has gained the researchers’ interest. A variety of previous works aimed to address the SER problem by two main approaches: on the one hand there exist the feature-based combined with the machine learning classifiers methods, while on the other, novel pipelines employing deep learning classifiers have also proven to be effective for emotion recognition. However, the lack of the existing approaches in the exploitation of the dynamic nature of the voice and further, the speech, aroused our curiosity of how the mathematical tool of the recurrence quantification analysis (RQA) and its extracted recurrence plots (RPs) and features could treat the SER problem. Consequently, the purpose of this work is to exploit the RPs and through the use of the RQA features construct a multi-channel convolutional neural network (CNN) which can differentiate the primary emotions, described in the German EMODB database. Our experimental results proved that our proposed pipeline outperforms the existing literature’s classification results, reaching an unweighted average recall (UAR) score up to 94%.

The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.