Real time Emotion Detection System in healthcare

A person?s good health requires that he is mentally prosperous. And if he is mentally calm, he will be able to do good work, or on the other side if he is suffering from any mental problem such as work stress or any other personal problem which he cannot share in office, then it effects on his work.

2025-06-28 16:34:42 - Adil Khan

Project Title

Real time Emotion Detection System in healthcare

Project Area of Specialization Artificial IntelligenceProject Summary

A person’s good health requires that he is mentally prosperous. And if he is mentally calm, he will be able to do good work, or on the other side if he is suffering from any mental problem such as work stress or any other personal problem which he cannot share in office, then it effects on his work. So, therefore, I want to create a system which can detect person’s emotions. Because emotions and expressions tell how actually a one person is feeling, he is in happiness, sadness, fear, disgust, anger, neutral or surprise. This system recognizes user’s emotions in image processing using facial recognition and is designed as a Python based System. In which Emotions are detected in real time for the health care of the user, and this can be done by using Raspberry pi 3, So, emotion detection bases on information acquisition and processing from the several sensors placed in the environment to recognize some affective states(happy, sad, anger, fear, disgust, neutral or surprise), and camera to acquire facial emotions, camera to track the behavior, and this is a window 10 IoT based project. Realtime Emotion Detection System in Healthcare is an Artificial Intelligence based project and uses one type of Artificial intelligence that is deep learning, uses algorithms of Deep learning such as ANN (Artificial Neural Network), in which we design a system that captures human image through camera and recognize the emotions which are showed in image and after this display a result on the screen that are according to the picture of the person whose emotion is detected. If person is in bad emotional (e.g. in anger or in stress) sate then the system should show an alert.

Emotions can detect through speech but My project detects emotions by taking a picture and also display that picture on the screen.

Project Objectives

Our objective is to detect the Emotions of human face in real time as fast and as accurate as possible, that are useful in healthcare.Emotion detection is essential and useful in human computer and human robot interaction applications because emotions indicate feelings and needs. Our application can recognize user emotions to provide appropriate services in healthcare.

This project is proved beneficial in health because thorough this device doctors can check patients’ emotions and realizes that the patient is in anger or in stress and the treatment of the patient can starts immediately. Thus, you can say that this system can save the doctors time.

Project Implementation Method

Real Time Emotion Detection System in healthcare implementation methods are:

The emotion detection finds a user’s face from the video

frames (input). 

• The detection extracts the facial features and normalizes

them to form feature vectors. 

• It then classifies the user emotions into one of seven classes

(neutral, happiness, sadness, anger, disgust, fear and

surprise) using a classifier that is generated from training

process. 

• Finally, it calculates the percentage of each emotion for

The emotion detection finds a user’s face from the video

frames (input). 

• The detection extracts the facial features and normalizes

them to form feature vectors. 

• It then classifies the user emotions into one of seven classes

(neutral, happiness, sadness, anger, disgust, fear and

surprise) using a classifier that is generated from training

process. 

• Finally, it calculates the percentage of each emotion for

The emotions detection finds a user’s face from video frames. The detection extracts from the facial feature and normalizes them to form feature vectors. It then classifies the user emotions into one of seven classes happiness, sadness, fear, disgust, anger, neutral or surprise using a classifier that is generating from training process. Finally, it calculates the percentage of each emotion for further analysis.

 Classifying the emotion on the face as happy, angry, sad, neutral, surprise, disgust or fear.

To detect emotion in the

Tools Used:

There are different tools which are used for the implementation of the project. These tools are as follows:

Languages:

Deep Leaning

Benefits of the Project

Emotion Detection System in Health Care can give the following benefits:

One of the benefits of this system is that people can see their emotions themselves and tries to overcome if they are in anger state. Emotion Detection System can be used to determine levels of stress while handling machines and vehicles and helps to reduce the risk of car crashes or accidents at work.

Technical Details of Final Deliverable

This Project is an Emotion based. So, in final it is a kind of artificial Intelligence based machine which simply takes inputs from the user and show the output to the user. And this can be done by using different algorithms that are part of artificial intelligence, machine learning, and deep learning. These algorithms used to detect emotions happiness, sadness, fear, disgust, anger, neutral or surprise that are important for human healthcare. The final delivery of the project is a hardware based which is connected with raspberry pi 3 and LCD screen or TAB and camera and sensors.

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries Education , 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
Raspberry pi 3 Equipment11600016000
camera Equipment190009000
sensors Equipment710007000
Laptop Equipment13000030000
Printing report Miscellaneous 120002000
Micro SD Memory Card Miscellaneous 120002000
USB flash Miscellaneous 120002000
tab Equipment180008000
stationary Miscellaneous 140004000

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