Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognit...

Full description

Saved in:
Bibliographic Details
Published inApplied sciences Vol. 13; no. 9; p. 5402
Main Authors Estrada, Jheanel E, Vea, Larry A, Devaraj, Madhavi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
Subjects
Back pain
Background noise
Body parts
Computer vision
convolutional neural network
Coronaviruses
COVID-19
Datasets
decision tree
Deep learning
expert systems
HCI
Home environment
Interactive systems
machine learning
Machine vision
Neck pain
Neural networks
Occlusion
Pandemics
Posture
Recognition
Rehabilitation
Smartphones
Surveillance
Telecommuting
Online AccessGet full text

Cover

More Information
Summary:Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points’ distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13095402