Proposed a Framework for Depression Monitoring System by Detecting the Facial Expression using Soft Computing Algorithm
Open Access
- 10 May 2021
- journal article
- Published by Lattice Science Publication (LSP) in International Journal of Preventive Medicine and Health
- Vol. 1 (2), 5-7
- https://doi.org/10.54105/ijpmh.b1003.051221
Abstract
Healthcare Informatics plays a very important role for manipulating data. In the healthcare discoveries, pattern recognition is important for the prediction of depression, aggression, pain and severe disease diagnostics. In [16][5], the real innovation that has affected and organized human services is cloud computing, which empowers whenever anyplace access to the information put away in a cloud. The mobile devices are continuously observing patients that move around a networked healthcare environment. In traditional healthcare diagnostic system, we depend upon expensive tests and machineries which increase the expenditure of healthcare. It is dire need to reduce the aggregate cost of regular or usual diagnostics incorporates high cost of hospitalization. These expenses can be limited or disposed of with the assistance of remote patient monitoring gadget, a healthcare IoT product. Remote monitoring of person’s health gadget includes the observing of a person from an alternate area. This dispenses the requirement for driving to clinic and to being hospitalized for less severe circumstances. This research will explore the depression monitoring system by detecting the facial expression using suitable soft computing algorithm. We may use different algorithms such as CNN and Multilayer Perceptron to get the best result. On the basis of classification it detects the class of disease. For this purpose, the primary dataset from various facial expressions of a patient will be collected, filtered and apply to classification algorithm to train the model.Keywords
This publication has 11 references indexed in Scilit:
- Internet of Things for Smart Healthcare: Technologies, Challenges, and OpportunitiesIEEE Access, 2017
- Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal ExpressionsIEEE Transactions on Cognitive and Developmental Systems, 2017
- Facial geometry and speech analysis for depression detection2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017
- Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity DataSensors, 2017
- A Design Characteristics of Smart Healthcare System as the IoT ApplicationIndian Journal of Science and Technology, 2016
- A framework for depression dataset to build automatic diagnoses in clinically depressed Saudi patientsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Deep learningNature, 2015
- Facial Behavior Recognition Using Soft Computing Techniques: A SurveyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Detecting depression from facial actions and vocal prosodyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Computer-assisted assessment of depression and function in older primary care patientsComputer Methods and Programs in Biomedicine, 2003