Proposed a Framework for Depression Monitoring System by Detecting the Facial Expression using Soft Computing Algorithm

Healthcare Informatics plays a very important rolefor manipulating data. In the healthcare discoveries, patternrecognition is important for the prediction of depression,aggression, pain and severe disease diagnostics. In [16][5], thereal innovation that has affected and organized human services iscloud computing, which empowers whenever anyplace access tothe information put away in a cloud. The mobile devices arecontinuously observing patients that move around a networkedhealthcare environment. In traditional healthcare diagnosticsystem, we depend upon expensive tests and machineries whichincrease the expenditure of healthcare. It is dire need to reduce theaggregate cost of regular or usual diagnostics incorporates highcost of hospitalization. These expenses can be limited or disposedof with the assistance of remote patient monitoring gadget, ahealthcare IoT product. Remote monitoring of person’s healthgadget includes the observing of a person from an alternate area.This dispenses the requirement for driving to clinic and to beinghospitalized for less severe circumstances. This research willexplore the depression monitoring system by detecting the facialexpression using suitable soft computing algorithm. We may usedifferent algorithms such as CNN and Multilayer Perceptron toget the best result. On the basis of classification it detects the classof disease. For this purpose, the primary dataset from variousfacial expressions of a patient will be collected, filtered and applyto classification algorithm to train the model.