tinyml 0th review (1)

TINYML: A HUMAN ACTIVITY DETERMINATION PREDICTING
ABNORMALITY FOR MINING FIELD WORKERS

•Problem Statement
•Objective
•Literature Review
•Introduction/Abstract
•Existing System
•Disadvantages of Existing System
•Proposed System
•Advantages of Proposed System
•Architecture
•Hardware Requirements
•Software Requirements
•Applications
•References
CONTENTS

PROBLEM STATEMENT
•No protection system designed for field workers condition monitoring
•Loss of life due to unpredicted condition of workers
•Costlier activity prediction system

4
OBJECTIVE
•To effectively develop a TinyML concept for the field workers.
•To implement an early alerting system to save the life of the workers.
•To implement long distance data transmission in areas with no internet connectivity.

5
ABSTRACT/INTRODUCTION
•In many countries more than half of workers are employed in the informal sector with no
social protection for seeking health care and lack of regulatory enforcement of occupational
health and safety standards.
•Employment and working conditions have powerful effects on health equity.
•The health of workers is an essential prerequisite for household income, productivity and
economic development.
•Health risks at the workplace, such as workers working in high zones experience oxidation
problems, body heat problems, risk of falling down and so on.
•Conditions of employment, occupation and the position in the workplace hierarchy also affect
health.
•These conditions needs to be monitored then and there to avoid risking the worker life.
•In this project we provide a solution where the condition of the worker in terms of oxidation,
heart rate, temperature and position is monitored and given to a machine learning model
deployed in a chip to predict the worker condition.
•An independent mobile app is developed to provide notifications about abnormalities.
•Thus this project provides efficient worker monitoring system saving their life.