Computer vision based working environment monitoring to analyze Generalized Anxiety Disorder (GAD)

Ever advancing development in Computer Vision and Deep Learning has increased the efficacy of smart monitoring by analyzing and predicting the physical abnormalities and generating time-sensitive results. Based on the improved principles of smart monitoring and data processing, a novel computer vision assisted deep learning based posture monitoring system is proposed to predict Generalized Anxiety Disorder (GAD) oriented physical abnormalities of an individual from their working environment. We used deep learning-assisted 3D Convolutional Neural Network (CNN) technology for spatio-temporal feature extraction and Gated Recurrent Unit (GRU) model to exploit the extracted temporal dynamics for adversity scale determination. The alert-based decisions with the deliverance of the physical state helps to increase the utility of the proposed system in the healthcare or assistive-care domain. The proficiency of the system is also enhanced by storing the predicted anomaly scores in the local database of the system which can be further used for therapeutic purposes. To validate the prediction performance of the proposed system, extensive experiments are conducted on three challenging datasets, NTU RGB+D, UTD-MHAD and HMDB51. The proposed methodology achieved comparable performance by obtaining the mean accuracy of 91.88%, 94.28%, and 70.33%, respectively. Furthermore, the average prediction time taken by the proposed methodology is approximately 1.13 seconds which demonstrates the real-time monitoring efficiency of the system. The calculated outcomes show that the proposed methodology performs better contrasted with other contemporary studies for activity prediction, data processing cost, error rate, and time complexity.