emotional states detection from physiological signals has many potential applications. In Human-Machine or Human-Human interaction systems, stress detection could provide users with improved services and can be a tool for monitoring and preventing potential stress-related pathologies. Traditional machine learning techniques for automatic stress recognition have been used in previous researches but they sometimes present specific limitations. The emergence of deep learning permits the reveal of underlying patterns in body response witch, otherwise would not be easily observed. In this paper we explore the application of Long Short-Term Memory (LSTM) and Deep Neural (DNN) Networks for real time stress monitoring in young gamers. We base our study on their body responses. For this, we use physiological signals such as the electrocardiography (ECG), the electrodermal activity (EDA), and the electromyography (EMG), measured by non-invasive wearable sensors. The result of the study provides an evaluation of both models’ capacity in predicting real time gamers’ emotional state built on the variation of their physiological parameters.

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies.

Speech emotion recognition (SER) is a new emergent field of research that has several possible applications in both human-computer and human-human interaction systems. The body of work on emotion detection from speech signal is relatively limited. Nowadays, researchers are yet augmenting on what features effects the identification of emotion in speech. There is a significant ambiguity as to the finest algorithm for emotion's classification, and which emotions to class together as well. In this work, we seek to address these matters. We use Support Vector Machines (SVMs) and Artificial Neuron Network (ANN) to classify opposite emotions. There is a variability of temporal and spectral characteristics that can be extracted from human speech. We focus only on Mel Frequency Cepstral Coefficients (MFCCs) as inputs to the classification algorithms. The classification reports obtained from the conducted experiments allow us to say that, for the given parameters, the ANN model was better detecting the speech carried emotional information than the SVM.

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesi-ty-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist-hip ratio, through infra-red-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse popula-tions, and scalability for widespread deployment in existing health workflows Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multi-modal data harmonization and portable, real-time analytics. Future priorities involve standardiz-ing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shift-ing obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies.