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1. TYPE THE SUBJECT NAME HERE
SUBJECT CODE
II III
DEEP LEARNING BASED HEALTH
MONITORING SYSTEM
ZEROTH REVIEW
DATE : 16/11/2021
STUDENT MEMBER : NITHYA S
REG. NO : 141920403004
GUIDED BY: Dr. J. RAJA (HOD, ECE)
20PCOPJ301
Innovative Design Project - III

2. OVERVIEW
● Problem definition
● Objective
● Literature Review
● Implementation Methodology
● Sustainable Development Goals – Category
● Timeline
2
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3. 3
PROBLEM DEFINITION
• India is one of the lowest ranking country in
consuming unhealthiest packaged food.
• There is an evident growing trend in consuming
un-healthy packaged food such as noodles, savory
snacks, biscuits etc..
• The Indian packaged food market is expected to be
double and grow up to USD 70 billion in the next 5-
10 years. Source: Business Standard (BS- Feb, 2021)
20PCOPJ301
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4. 4
OBJECTIVE
PROJECT WORK
• The objective of this project is to monitor the level of Fat, Transfat through consumption of
packaged food.
• This project will use the most powerful machine learning technique from the field of deep
learning to recognize and classify the nutritional facts in each of the Packaged food
images using a pre trained Convolutional Neural Network (CNN) as a feature extractor to
train an image category classifier.
• The data such as Fat, Trans-Fat will be compared with the permissible limit as advised by
the WHO (based on Age & Gender). The user will be informed once the Threshold level is
reached through SMS/ WhatsApp.
• Beyond the threshold level consumption, the user will be also advised on the physical
excise dynamics (such as Walking, Cycling) need to burn the same to maintain their health
and well-being.
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ZEROTH REVIEW

5. LITERATURE REVIEW
S. No Paper title Publication & Year Author
Inference used for
project
1
An Efficient CNN
Architecture for Image
Classification
IEEE, 04 October 2018
Divya Kiran,
Hariharan
Ramasangu,
Shahmustafa
Mujawar
Study &
understanding of
CNN In image
processing
2
Review of Deep
Learning Algorithms
and Architectures
IEEE, 22 April 2019
Ajay Shrestha,
Ausif Mahmood
Study &
understanding of
Deep Learning
3 Machine learning
methods for IoT and
their Future
Applications
IEEE, 30 January 2020 Mansi Jindal,
Jatin Gupta,
Bharat Bhushan
ML-IoT interface
and application
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6. IMPLEMENTATION METHODOLOGY
IMAGE
CAPTURING
ML-DEEP LEARNING
HEALTH MONITORING
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7. CATEGORY
Satisfies…………
SUSTAINABLE DEVELOPMENT GOALS
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8. SCHEDULE PROGRESS
10th Oct- 20th Oct 2021 Title Discussion & Confirmation
21st Oct - 30th Oct 2021 Literature Survey & Feasibility Study
1st Nov- 15th Nov 2021 Study on concepts
16th Nov 2021 Zeroth Review
TIMELINE OF THE WORK
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9. THANK YOU!
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