International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Infant Care Assistant with Emotion Detection-Using Machine Learning, Image Processing & IOT Sensor Network Prerana Rout1, Lucky Upadhyay2, Sai Abhishek Babu3, Faloudeep Gupta4, M Sindhu Sree5 12345Dept.
of ECE, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------with an initial low-cost E-Baby Cradle that would rock on its Abstract - With evolving technology and busy working culture, there is a need of smart device for working parents to help them in monitoring and assisting their child. Infant Care Assistant is such a smart device consisting of IOT Sensor Network, a microcontroller and Raspberry pi to collect information on the current state of the child and its surroundings and soothe it accordingly with automated techniques. Addition to it, an emotion recognition model using machine learning has also been included to detect the face of the child and predict its emotion. Moreover, an app called Blynk has been used for graphical user interface. this model could help reducing workload of busy working parents in taking utmost care of their child.
own when the baby cries where the speed of the cradle can be regulated. The system consists of an alarm which specify wet mattress of the baby and its loud cry. Baby cry detection in domestic environment using deep learning (Yizhar Lavner, Rami Cohen, Dima Ruinskiy, Hans Ijzerman) [3] includes the use of two machine-learning algorithms for automatic detection of baby cry one is logistic regression classifier & the other is CNN classifier. In Image Processing Techniques to Recognize Facial Emotions (A. Mercy Rani, R. Durgadevi) [4] they proposed an emotion recognition system which included face detection and morphological processing using viola jones algorithm.
Key Words: Automation, Sensors, Machine Learning,
3. BLOCK DIAGRAM AND WORKING PRINCIPLE
IOT, Infant Care
There are 3 key features of the infant care Assistant i.e., Data acquiring, Infant soothing and emotion recognition. The data acquiring unit consists of IOT Sensor network comprising of various sensors and a node micro controller unit ESP8266 to acquire data related to the child and its surroundings. Here ESP8266 mcu is an open-source software and hardware development environment built on low-cost chip, designed and manufactured by espressif containing its own CPU, ram and wi-fi. This mcu is integrated with various sensors such as a dht11 sensor which is used to detect the humidity and temperature of the infant surroundings, a noise sensor used for detecting infant cry noise, a moisture sensor for detecting the presence of wetness in the child’s bed. all the readings of these sensors are then reflected on a freely available android/iOS app called blynk using which parents can monitor their child.
1. INTRODUCTION In the growing era of technology where everything around us is getting advanced and intelligent day by day by the means of artificial intelligence, internet of things and various smart devices such as smartphones, smart TVs, smart watches, smart home appliances and many other devices, then why not a smart assistant?? In this busy world where now a days both the parents are working and have little infants or first-time parents who just entered parenthood and don’t have enough experience are facing difficulties in providing sufficient time to the infants provided infants need proper attention and care maximum of the time.so in order to cope with this a solution or a device is need of the time.
Infant soothing unit comprises of a baby cradle made of metal which would rock by the help of a servo motor and 2 channels relay, when the noise sensor is activated. if the humidity crosses a certain threshold set by the user, the fan would automatically get on. If the moisture sensor senses moisture, then it would reflect on the blynk app saying moisture detected. hence these automated techniques ensure the calming of a troubled child.
A smart assistant is needed to provide proper assistance to the infants in the absence of parents as in monitors the infants all the time, acquire information related to them, send notifications if any attention is required and perform real time interactions between the parents and their child.
2. LITERATURE SURVEY
The emotion recognition feature includes a raspberry pi module with an attached pi camera. A machine learning code has been developed based on python which on running would trigger the camera to get on and detect the real-time image of the child and predict the emotion whether it is happy or sad or angry or yawning etc. on the window of the terminal.
A Method for Face Segmentation, Facial Feature Extraction and Tracking (Samir K. Bandyopadhyay) [1] presents the comparison between the methodologies used for human face segmentation from face images based on textural analysis and KNN classifier. In Automatic E-Baby Cradle Swing Based on Baby Cry (Misha Goyal and Dilip Kumar) [2] they came up
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