This research explored the integration of the real-time machine learning classification of motor imagery data with a brain–machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive–motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.

Mental health disorders have a high prevalence in Latin America (LATAM), with an estimated 6.7% of the population suffering from anxiety and 4% from depression. Digital mental health interventions (DMHIs) have been implemented to address these issues. However, there has yet to be a clear picture of these interventions in the region. This scoping review aims to analyze DMHIs’ characteristics and the barriers and facilitators for their implementation in five LATAM countries (Brazil, Chile, Colombia, Mexico, and Peru). To achieve this goal, four databases (PubMed, APA PsycNet, Scielo, and LILACS) were searched using relevant search terms in English, Spanish, and Portuguese. A total of 484 references were identified and narrowed down to 15 articles included in the final analysis. The studies mostly consisted of RCTs and mixed-methods studies. Most of the DMHIs were designed for individuals, with a focus on targeted communication and personal health tracking. Interventions targeting healthcare professionals were less common, primarily focusing on decisional support. The most prominent barriers to the successful implementation of DMHIs included insufficient healthcare professional training (40%) and resistance from clinicians and organizational culture (40%), while common facilitators included easy access to the interventions (46.7%) and increased training and support for clinical teams (46.7%). Positive outcomes were reported in terms of both effectiveness (33.3%), with many interventions showing results equal to or better than traditional methods (33.3%), and patient satisfaction (33.3%).

Mental Health disorders have a high prevalence in Latin America (LATAM), with an estimated 6.7% of the population suffering from anxiety and 4% from depression. Digital Mental Health Interventions (DMHIs) have been implemented to address these issues. However, there has yet to be a clear picture of these interventions in the region. This scoping review aims to analyze DMHIs characteristics and the barriers and facilitators for their implementation in five LATAM countries (Brazil, Chile, Colombia, Mexico, and Peru). To achieve this goal, four databases (PubMed, APA PsycNet, Scielo, and LILACS) were searched using relevant search terms in English, Spanish, and Portuguese. A total of 484 references were identified and narrowed down to 15 articles included in the final analysis. The studies mostly consisted of RCTs and mixed-methods studies. Most of the DMHIs were designed for individuals, with a focus on targeted communication and personal health tracking. Interventions targeting healthcare professionals were less common, primarily focusing on decisional support. The most prominent barriers to the successful implementation of DMHIs included insufficient healthcare professional training (40%) and resistance from clinicians and organizational culture (40%), while common facilitators included easy access to the interventions (46.7%) and increased training and support for clinical teams (46.7%). Positive outcomes were reported in terms of both effectiveness (33.3%), with many interventions showing results equal to or better than traditional methods (33.3%), and patient satisfaction (33.3%).