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.

The lack of helmet use in motorcyclists is one of the main risk factors with severe consequences in traffic accidents. Wearing a certified motorcycle helmet can reduce the risk of head injuries by 69% and fatalities by 42%. At present there are systems that detect the use of the helmet in a very precise way, however they are not robust enough to guarantee a safe journey, that is why is proposed an intelligent model for detecting the helmet in real time using training images of a camera mounted on the motorcycle, and convolutional neural networks that allow constant monitoring of the region of interest to identify the use of the helmet. As a result, a model was obtained capable of identifying when the helmet is used or not in an objective and constant manner while the user is making a journey, with a performance of 97.24%. Thus, it was possible to conclude that this new safety perspective provides a first approach to the generation of new preventive systems that help reduce accident rates in these means of transport. As future work, it is proposed to improve the model with different images that may violate the helmet detection.