Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1

Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3

Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3

Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3

An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3

Triplet loss for Chromosome Classification
Volume-4 | Issue-1

Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4

Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2

Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1

Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3

A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1

COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1

Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1

Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4

Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4

State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2

Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4

OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4

VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2

Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4

Home / Archives / Volume-6 / Issue-3 / Article-2

Volume - 6 | Issue - 3 | september 2024

Smart Wearable Device for Enhancing Safety and Efficiency of Coal Miners
Jasmine K.  , Sanjith Krishna S., Subashini B., Swethaa Shree V., Rajavarma R.
Pages: 235-243
Cite this article
K., Jasmine, Sanjith Krishna S., Subashini B., Swethaa Shree V., and Rajavarma R.. "Smart Wearable Device for Enhancing Safety and Efficiency of Coal Miners." Journal of Innovative Image Processing 6, no. 3 (2024): 235-243
Published
21 June, 2024
Abstract

The task of ensuring worker safety in underground coal mines has never been simple. It has always been challenging to ensure worker safety in underground coal mines. Coal miners are seriously injured or killed as a result of numerous fatal and non-fatal accidents all over the world. Accidents occur as a result of lack of monitoring of mining areas and failure to implement proper safety measures. In this review, the coal miner's smart wearable safety device is implemented. A smart wearable safety device that will monitor the miner's health and provides precautionary measures for the miner's safety has been developed with the advent of the Industrial Internet of Things (IIoT). Integrating smart wearable safety devices with various health sensors such as a pulse rate sensor, temperature sensor, blood oxygen sensor, gas sensor, and camera, and then connecting it to Node MCU, and the internet enhances the safety of coal miners. Sensors constantly transmit sensor data to the cloud, and if an unusual situation arises, it notifies the responsible person in the control room as well as the miners. Since deep underground mining is especially susceptible to toxic gases, low levels of oxygen, and hazardous gases, the MQ gas sensor family can detect them. The proposed system is primarily used to improve working conditions inside coal mines and to ensure workers' safety.

Keywords

Coal Miner Safety Wearable Device Sensors Camera IIoT

×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

To access the full PDF, please complete the payment process.

Subscription Details

Category Fee
Article Access Charge
15 USD
Open Access Fee 100 USD
Annual Subscription Fee
200 USD
Subscription form: click here