Espino-Salinas Carlos H, Luna-García Huizilopoztli, Cepeda-Argüelles Alejandra, Trejo-Vázquez Karina, Flores-Chaires Luis Alberto, Mercado Reyna Jaime, Galván-Tejada Carlos E, Acra-Despradel Claudia, Villalba-Condori Klinge Orlando
Laboratorio de Tecnologías Interactivas y Experiencia de Usuario, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico.
Centro de Investigación e Inovación Biomedica e Informática, Unidad Academica de Ingeniería Electrica, Zacatecas 98000, Mexico.
Diagnostics (Basel). 2025 Jan 30;15(3):319. doi: 10.3390/diagnostics15030319.
: 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. : 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. : 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. : 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.
本研究提出了一种卷积神经网络（CNN）方法，用于利用表示为图像的运动活动模式来检测抑郁症和精神分裂症。参与者的运动活动数据被捕获并转换为视觉表示，从而能够使用先进的计算机视觉技术对这些精神障碍进行分类。该模型的性能通过三倍交叉验证进行评估，平均准确率达到95%，证明了所提出方法在准确识别心理健康状况方面的有效性。该研究的目的是开发一种模型，该模型能够通过使用CNN处理运动数据来识别不同的精神障碍，以便为专门从事这些心理状况诊断和有效治疗的领域提供一种支持工具。：该方法包括将运动活动数据进行分割并转换为图像，随后在这些视觉表示上进行CNN训练和测试阶段。这种创新方法能够识别微妙的运动行为模式，这些模式可能指示特定的精神状态，而无需侵入性干预或自我报告。：结果表明，CNN能够捕捉运动活动中的判别特征，以区分患有抑郁症、精神分裂症的个体和未被诊断为心理健康问题的个体。：这些发现强调了计算机视觉和深度神经网络技术在早期、非侵入性心理健康障碍诊断方面的潜力，对开发心理健康支持工具具有重要意义。
