An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolon...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
Main Authors: Arshad, Jehangir, Asim, Hafiza Mahnoor, Ashraf, Muhammad Adil, Jaffery, Mujtaba Hussain, Zaidi, Khurram Shabih, Amentie, Melkamu Deressa
Format: Journal Article
Language:English
Published: New York Hindawi 18-08-2022
John Wiley & Sons, Inc
Hindawi Limited
Subjects:
Accuracy
Algorithms
Back pain
Bayesian analysis
Classification
Comparative analysis
Data mining
Decision trees
Economic aspects
Field programmable gate arrays
Learning algorithms
Literature reviews
Machine learning
Neural networks
Offices
Posture
Pressure ulcers
Sensors
Skin
Software
Support vector machines
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Summary:In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.
Bibliography:ObjectType-Article-1
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Academic Editor: Mohamed Abdelaziz
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/7957148