Background/Objectives: This study presents a Convolutional Neural Network (CNN) approach for detecting depression and schizophrenia using motor activity patterns represented as images. Participants’ motor activity data were captured and transformed into visual representations, enabling advanced computer vision techniques for the classification of these mental disorders. The model’s performance was evaluated using a three-fold cross-validation, achieving an average accuracy of 95%, demonstrating the effectiveness of the proposed approach in accurately identifying mental health conditions. The objective of the study is to develop a model capable of identifying different mental disorders by processing motor data using CNN in order to provide a support tool to areas specialized in the diagnosis and efficient treatment of these psychological conditions. Methods: The methodology involved segmenting and transforming motor activity data into images, followed by a CNN training and testing phase on these visual representations. This innovative approach enables the identification of subtle motor behavior patterns, potentially indicative of specific mental states, without invasive interventions or self-reporting. Results: The results suggest that CNNs can capture discriminative features in motor activity to differentiate between individuals with depression, schizophrenia, and those without mental health diagnoses. Conclusions: These findings underscore the potential of computer vision and deep neural network techniques to contribute to early, non-invasive mental health disorder diagnosis, with significant implications for developing mental health support tools.

Engineering laboratory courses are essential for developing conceptual understanding and practical skills; however, the time students spend assembling prototypes and troubleshooting wiring issues often reduces opportunities for analysis, programming, and reflective learning. To address this limitation, this study designed and evaluated an integrated STM32-based educational laboratory that consolidates the main peripherals required in a microcontroller course into a single Printed Circuit Board (PCB) platform. A quasi-experimental intervention was implemented with 40 engineering students divided into a control group using traditional STM32 Blue Pill and breadboard connections and an experimental group using the integrated platform. Throughout ten laboratory sessions, data were collected through pre- and post-tests, laboratory logs, and the Motivated Strategies for Learning Questionnaire Short Form (MSLQ-SF). Results showed that the experimental group achieved a Hake normalized learning gain of 40.09% compared with 16.22% in the control group, also showing that it completed the sessions an average of 27 min faster and facilitated a substantial reduction in hardware- and connection-related errors. Significant improvements were also observed in metacognitive and improved motivational and self-regulated learning scores. Overall, the findings indicate that reducing operational barriers in laboratory work enhances both cognitive and motivational learning processes, supporting the adoption of integrated educational hardware to optimize learning outcomes in engineering laboratory courses.

Background: Parkinson’s disease (PD) is a chronic, slowly progressive, and irreversible neuropathological disorder characterized by the progressive degeneration of specific neurons responsible for producing neurotransmitters essential for motor control. Although PD primarily affects motor function, various non-motor symptoms commonly emerge during the prodromal phase. These include autonomic dysfunction, cognitive and neurobehavioral disorders, and sensory and sleep abnormalities. Notably, speech and voice alterations, particularly hypokinetic dysarthria, are frequent manifestations. This research presents a methodology to distinguish between individuals with PD and healthy controls using voice signals through speech recognition and machine learning (ML) techniques. A dataset comprising 81 voice samples (41 healthy controls and 40 PD patients) was utilized to extract two types of cepstral features: Mel-frequency cepstral coefficients (MFCCs) and subband-based cepstral coefficients (SBCs). These extracted features were used to train and evaluate three ML algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Results: Among the algorithms evaluated, the RF-SBC model exhibited the highest performance, achieving an accuracy of 96%, a sensitivity of 91.67%, and an Area Under the ROC Curve (AUC-ROC) of 98.08%. Conclusions: This study highlights the potential of integrating cepstral features with machine learning algorithms to develop reliable, non-invasive tools for the early detection of PD.

Background: Parkinson’s disease (PD) is a chronic, slowly progressive, and irreversible neuropathological disorder characterized by the progressive degeneration of specific neurons responsible for producing neurotransmitters essential for motor control. Although PD primarily affects motor function, various non-motor symptoms commonly emerge during the prodromal phase. These include autonomic dysfunction, cognitive and neurobehavioral disorders, and sensory and sleep abnormalities. Notably, speech and voice alterations, particularly hypokinetic dysarthria, are frequent manifestations. This research presents a methodology to distinguish between individuals with PD and healthy controls using voice signals through speech recognition and machine learning (ML) techniques. A dataset comprising 81 voice samples (41 healthy controls and 40 PD patients) was utilized to extract two types of cepstral features: Mel-frequency cepstral coefficients (MFCCs) and subband-based cepstral coefficients (SBCs). These extracted features were used to train and evaluate three ML algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Results: Among the algorithms evaluated, the SVM-SBC model exhibited the highest performance, achieving an accuracy of 79%, a sensitivity of 75.5%, and an Area Under the ROC Curve (AUC-ROC) of 84%. Conclusions: This study highlights the potential of integrating cepstral features with machine learning algorithms to develop reliable, non-invasive tools for the early detection of PD.

Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ($n=5$), moderate ($n=22$), and high ($n=60$) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research.