Effective Human Emotion Detection for Health Care Industry

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon.Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancemen

2025-06-28 16:26:56 - Adil Khan

Project Title

Effective Human Emotion Detection for Health Care Industry

Project Area of Specialization Artificial IntelligenceProject Summary

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon.Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. Recognizing or detecting feelings in health care data gives important and helpful information regarding the emotional state of patients. To recognize or detection of patient’s emotion against a specific disease using facial expression is a challenging task. Our proposed work will be helpful to automatically detect a patient’s emotion during disease and to avoid extreme acts like suicide, mental disorders, or psychological health issues.

Moreover, the detection of COVID-19 patients' health state can be obtained utilizing their motions.

Project Objectives

Objectives of the project are as follows:

Automatic feedback system:

            The main objective of the proposed system to automate the feedback of the patient. By using emotion detection system doctor can check that the patient is improving his/her health or not.

 This will be time consuming.

Build Smart health care centers:

Automatically identifying the emotions can help build smart health care centers that can detect depression and stress among the patients in order to start the medication early.

Improve patient’s safety:

         Our proposed work will be helpful to automatically detect a patient’s emotion during disease and to avoid extreme acts like suicide, mental disorders, or psychological health issues.

Easy and fast Identification:

         Check-in and check-out are fundamental setup. Face recognition technology can make them easier and faster. also decrease the work load for hospital staff at same time. When a patient enters, the face recognition system scans their face and run it against the hospital database.

Project Implementation Method

Implementation of the project consist of different steps:

Camera and sensor will be used. camera will be used as a input device that take picture as a input sensior will firstdetect the image and the another sensor will calculate the emotion state of an indevedual(patient).

This base contains images that are used for comparison and recognizing emotion variations. The images are stored in the database. Every time an input is given to the system, it finds a relevant image from its knowledge base by comparing the stored pictures and the input to come up with an output.

This step enhances the input and removes different types of noises. After that, the input image will be resized, typically with the use of the eye selection method.

During this step, the system will find any differences between the input image and the stored images and will finally lead to the emotion recognition step.

This is the final step of the process. The comparison is made, and the final output is given depending on the differences found

Benefits of the Project

Benefits of proposed project as follows:

Technical Details of Final Deliverable

 Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields.

Our implementation will be divided into 3 parts:

  1. Face detection
  2. Feature extraction
  3. Classification using machine learning algorithms

Feature extraction was very important part of the experiment. The added distance and area features provided good accuracy for CK+ database.The additional features (distance and area) reduced the accuracy of the experiment for SVM.

The algorithm generalized the results from the training set to the testing set better than SVM and other algorithms. The results of the emotion detection algorithm gave average accuracy up to 86% for RAFD database and 87% for CK+ database.

At final stage our proposed systen automatically understand patients' pain intensity levels using the proposed facial emotion recognition system. Two emotion probabilities, “fearful” and “sad”, are determined to be closely related to the pain intensity level.

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries Medical , Health Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT)Sustainable Development Goals Good Health and Well-Being for PeopleRequired Resources
Item Name Type No. of Units Per Unit Cost (in Rs) Total (in Rs)
Total in (Rs) 80000
camera Equipment15000050000
sensor Equipment21000020000
miscellaneous Miscellaneous 11000010000

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