e-Framework for m-Health Detection and Control Using GNN

The integration of Information and Communications Technology (ICT) for mental Health (m-Health) detection and control in the smart framework (e-Framework) opens the opportunity for developing the intelligent Human-device Interaction (HDI) system. m-Health detection using multi-modality cues: Respiration Wave Pattern (RWP), Heart Wave Pattern (HWP), and visual RGB facial expression of a person convey more accurate information to build a robust and efficient system for an early stage of m-Health recognition. This study proposes a k-nearest neighbor (k-NN) based Graph Neural Network (GNN) with the input domain modality of RGB, RWP, and HWP to interpret the six discrete m-Health, such as Anger, Fear, Disgust, Joy, Sad, and Surprise. The average classification performance of our proposed k-NN-GNN architecture is 98. 94% for our in-house experimental dataset, validated with the subjective evaluation ground truth data of SAM score, 98.04%, 98.07%, and 98.06% for AMIGOS, AMHUSE, and MAHNOB, respectively. A User Interface Andriod Application (UIA) connected to the e-Framework provides Negative Mental Health (NMH) distraction suggestions to the end user for an early stage of NMH detection and control.